
"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCAmelCase__      :    int    							= {
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      :    int    							= ['ConvBertTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      :    Union[str, Any]    							= [
					    'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'ConvBertForMaskedLM',
					    'ConvBertForMultipleChoice',
					    'ConvBertForQuestionAnswering',
					    'ConvBertForSequenceClassification',
					    'ConvBertForTokenClassification',
					    'ConvBertLayer',
					    'ConvBertModel',
					    'ConvBertPreTrainedModel',
					    'load_tf_weights_in_convbert',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      :    int    							= [
					    'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFConvBertForMaskedLM',
					    'TFConvBertForMultipleChoice',
					    'TFConvBertForQuestionAnswering',
					    'TFConvBertForSequenceClassification',
					    'TFConvBertForTokenClassification',
					    'TFConvBertLayer',
					    'TFConvBertModel',
					    'TFConvBertPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
					from .tokenization_convbert import ConvBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_convbert_fast import ConvBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_convbert import (
										    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ConvBertForMaskedLM,
										    ConvBertForMultipleChoice,
										    ConvBertForQuestionAnswering,
										    ConvBertForSequenceClassification,
										    ConvBertForTokenClassification,
										    ConvBertLayer,
										    ConvBertModel,
										    ConvBertPreTrainedModel,
										    load_tf_weights_in_convbert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_convbert import (
										    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFConvBertForMaskedLM,
										    TFConvBertForMultipleChoice,
										    TFConvBertForQuestionAnswering,
										    TFConvBertForSequenceClassification,
										    TFConvBertForTokenClassification,
										    TFConvBertLayer,
										    TFConvBertModel,
										    TFConvBertPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase__      :    int    							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






"""simple docstring"""




def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   [1]

							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   0, 0, 0
							SCREAMING_SNAKE_CASE__						:       List[str]           =   ugly_nums[ia] * 2
							SCREAMING_SNAKE_CASE__						:       int           =   ugly_nums[ia] * 3
							SCREAMING_SNAKE_CASE__						:       Any           =   ugly_nums[ia] * 5

							for _ in range(1   ,_snake_case						):
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   min(_snake_case   ,_snake_case   ,_snake_case						)
														ugly_nums.append(_snake_case						)
														if next_num == next_a:
																					ia += 1
																					SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ugly_nums[ia] * 2
														if next_num == next_a:
																					ia += 1
																					SCREAMING_SNAKE_CASE__						:       List[str]           =   ugly_nums[ia] * 3
														if next_num == next_a:
																					ia += 1
																					SCREAMING_SNAKE_CASE__						:       Tuple           =   ugly_nums[ia] * 5
							return ugly_nums[-1]


if __name__ == "__main__":
					from doctest import testmod

					testmod(verbose=True)
					print(f"""{ugly_numbers(2_0_0) = }""")




