






"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase				:				Tuple   		=		{
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Optional[Any]   		=		["ConvBertTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				List[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:
		_lowercase				:				Union[str, Any]   		=		[
		    "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

		_lowercase				:				Tuple   		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list[int]			):
			"""simple docstring"""
			if not numbers:
						return 0

			if not isinstance(__lowerCamelCase							,	(list, tuple)			) or not all(
			    isinstance(__lowerCamelCase							,	__lowerCamelCase			) for number in numbers			):
						raise ValueError('''numbers must be an iterable of integers'''			)

			lowerCamelCase__	:	Any				=numbers[0]

			for i in range(1							,	len(__lowerCamelCase			)			):
						# update the maximum and minimum subarray products
						lowerCamelCase__	:	Dict				=numbers[i]
						if number < 0:
									lowerCamelCase__			,     lowerCamelCase__	:	List[Any]				=min_till_now, max_till_now
						lowerCamelCase__	:	Optional[int]				=max(__lowerCamelCase							,	max_till_now * number			)
						lowerCamelCase__	:	Dict				=min(__lowerCamelCase							,	min_till_now * number			)

						# update the maximum product found till now
						lowerCamelCase__	:	Tuple				=max(__lowerCamelCase							,	__lowerCamelCase			)

			return max_prod




