


from typing import TYPE_CHECKING

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


lowerCAmelCase__ :			Tuple         ={
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__ :			Dict         =['CamembertTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__ :			Optional[Any]         =['CamembertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__ :			Dict         =[
				    'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'CamembertForCausalLM',
				    'CamembertForMaskedLM',
				    'CamembertForMultipleChoice',
				    'CamembertForQuestionAnswering',
				    'CamembertForSequenceClassification',
				    'CamembertForTokenClassification',
				    'CamembertModel',
				    'CamembertPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__ :			Tuple         =[
				    'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFCamembertForCausalLM',
				    'TFCamembertForMaskedLM',
				    'TFCamembertForMultipleChoice',
				    'TFCamembertForQuestionAnswering',
				    'TFCamembertForSequenceClassification',
				    'TFCamembertForTokenClassification',
				    'TFCamembertModel',
				    'TFCamembertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_camembert import CamembertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_camembert_fast import CamembertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_camembert import (
								    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CamembertForCausalLM,
								    CamembertForMaskedLM,
								    CamembertForMultipleChoice,
								    CamembertForQuestionAnswering,
								    CamembertForSequenceClassification,
								    CamembertForTokenClassification,
								    CamembertModel,
								    CamembertPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_camembert import (
								    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFCamembertForCausalLM,
								    TFCamembertForMaskedLM,
								    TFCamembertForMultipleChoice,
								    TFCamembertForQuestionAnswering,
								    TFCamembertForSequenceClassification,
								    TFCamembertForTokenClassification,
								    TFCamembertModel,
								    TFCamembertPreTrainedModel,
								)

else:
				import sys

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









def 	a__ (							A__ = 4_0_0_0_0_0_0							):

			if n <= 1:
						return 0
			SCREAMING_SNAKE_CASE_		:    Dict 		=				0
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				2
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				0
			while 4 * b + a <= n:
						SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    List[str] 		=				b, 4 * b + a
						count += a
			return count + b


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





