





"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCamelCase_     ={
    """configuration_mpnet""": ["""MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MPNetConfig"""],
    """tokenization_mpnet""": ["""MPNetTokenizer"""],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_     =[
						    """MPNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MPNetForMaskedLM""",
						    """MPNetForMultipleChoice""",
						    """MPNetForQuestionAnswering""",
						    """MPNetForSequenceClassification""",
						    """MPNetForTokenClassification""",
						    """MPNetLayer""",
						    """MPNetModel""",
						    """MPNetPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_     =[
						    """TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFMPNetEmbeddings""",
						    """TFMPNetForMaskedLM""",
						    """TFMPNetForMultipleChoice""",
						    """TFMPNetForQuestionAnswering""",
						    """TFMPNetForSequenceClassification""",
						    """TFMPNetForTokenClassification""",
						    """TFMPNetMainLayer""",
						    """TFMPNetModel""",
						    """TFMPNetPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
						from .tokenization_mpnet import MPNetTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mpnet_fast import MPNetTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mpnet import (
												    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MPNetForMaskedLM,
												    MPNetForMultipleChoice,
												    MPNetForQuestionAnswering,
												    MPNetForSequenceClassification,
												    MPNetForTokenClassification,
												    MPNetLayer,
												    MPNetModel,
												    MPNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mpnet import (
												    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMPNetEmbeddings,
												    TFMPNetForMaskedLM,
												    TFMPNetForMultipleChoice,
												    TFMPNetForQuestionAnswering,
												    TFMPNetForSequenceClassification,
												    TFMPNetForTokenClassification,
												    TFMPNetMainLayer,
												    TFMPNetModel,
												    TFMPNetPreTrainedModel,
												)

else:
						import sys

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










"""simple docstring"""




UpperCamelCase_     =1_000





def  a_       (							_lowercase     ):
						return x * x





def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase = 4     ):
						assert callable(
						    _lowercase     ), F"""the function(object) passed should be callable your input : {function}"""
						assert isinstance(_lowercase ,		(float, int)     ), F"""a should be float or integer your input : {a}"""
						assert isinstance(function(_lowercase     ) ,		(float, int)     ), (
						    "the function should return integer or float return type of your function, "
						    F"""{type(_lowercase     )}"""
						)
						assert isinstance(_lowercase ,		(float, int)     ), F"""b should be float or integer your input : {b}"""
						assert (
						    isinstance(_lowercase ,		_lowercase     ) and precision > 0
						), F"""precision should be positive integer your input : {precision}"""

						# just applying the formula of simpson for approximate integration written in
						# mentioned article in first comment of this file and above this function

						_UpperCamelCase    :      str						     =					(b - a) / N_STEPS
						_UpperCamelCase    :      Dict						     =					function(_lowercase     ) + function(_lowercase     )

						for i in range(1 ,		_lowercase     ):
												_UpperCamelCase    :      List[Any]						     =					a + h * i
												result += function(_lowercase     ) * (4 if i % 2 else 2)

						result *= h / 3
						return round(_lowercase ,		_lowercase     )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



