






from typing import TYPE_CHECKING

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


a__       =     {
    """configuration_mobilebert""": [
        """MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileBertConfig""",
        """MobileBertOnnxConfig""",
    ],
    """tokenization_mobilebert""": ["""MobileBertTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     ["""MobileBertTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MobileBertForMaskedLM""",
	    """MobileBertForMultipleChoice""",
	    """MobileBertForNextSentencePrediction""",
	    """MobileBertForPreTraining""",
	    """MobileBertForQuestionAnswering""",
	    """MobileBertForSequenceClassification""",
	    """MobileBertForTokenClassification""",
	    """MobileBertLayer""",
	    """MobileBertModel""",
	    """MobileBertPreTrainedModel""",
	    """load_tf_weights_in_mobilebert""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFMobileBertForMaskedLM""",
	    """TFMobileBertForMultipleChoice""",
	    """TFMobileBertForNextSentencePrediction""",
	    """TFMobileBertForPreTraining""",
	    """TFMobileBertForQuestionAnswering""",
	    """TFMobileBertForSequenceClassification""",
	    """TFMobileBertForTokenClassification""",
	    """TFMobileBertMainLayer""",
	    """TFMobileBertModel""",
	    """TFMobileBertPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_mobilebert import (
	    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    MobileBertConfig,
	    MobileBertOnnxConfig,
	)
	from .tokenization_mobilebert import MobileBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mobilebert_fast import MobileBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mobilebert import (
		    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MobileBertForMaskedLM,
		    MobileBertForMultipleChoice,
		    MobileBertForNextSentencePrediction,
		    MobileBertForPreTraining,
		    MobileBertForQuestionAnswering,
		    MobileBertForSequenceClassification,
		    MobileBertForTokenClassification,
		    MobileBertLayer,
		    MobileBertModel,
		    MobileBertPreTrainedModel,
		    load_tf_weights_in_mobilebert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_mobilebert import (
		    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFMobileBertForMaskedLM,
		    TFMobileBertForMultipleChoice,
		    TFMobileBertForNextSentencePrediction,
		    TFMobileBertForPreTraining,
		    TFMobileBertForQuestionAnswering,
		    TFMobileBertForSequenceClassification,
		    TFMobileBertForTokenClassification,
		    TFMobileBertMainLayer,
		    TFMobileBertModel,
		    TFMobileBertPreTrainedModel,
		)

else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








def _UpperCAmelCase   (	a						:	int  ,					a						:	Optional[Any] ):
							# "extended trapezoidal rule"
							# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
							snake_case__	  =       (boundary[1] - boundary[0]) / steps
							snake_case__	  =       boundary[0]
							snake_case__	  =       boundary[1]
							snake_case__	  =       make_points(a  ,					a  ,					a )
							snake_case__	  =       0.0
							y += (h / 2.0) * f(a )
							for i in x_i:
														# print(i)
														y += h * f(a )
							y += (h / 2.0) * f(a )
							return y

def _UpperCAmelCase   (	a						:	Dict  ,					a						:	Union[str, Any]  ,					a						:	Union[str, Any] ):
							snake_case__	  =       a + h
							while x < (b - h):
														yield x
														snake_case__	  =       x + h

def _UpperCAmelCase   (	a						:	Any ):  # enter your function here
							snake_case__	  =       (x - 0) * (x - 0)
							return y

def _UpperCAmelCase   (	):
							snake_case__	  =       0.0  # Lower bound of integration
							snake_case__	  =       1.0  # Upper bound of integration
							snake_case__	  =       10.0  # define number of steps or resolution
							snake_case__	  =       [a, b]  # define boundary of integration
							snake_case__	  =       method_a(a  ,					a )
							print(F'''y = {y}''' )


if __name__ == "__main__":
	main()
