






"""simple docstring"""
A_			: Optional[int]				=			10_00






def        lowerCamelCase_  (						_lowerCamelCase				):
 return x * x






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase = 4				):
 assert callable(
     _lowerCamelCase				), f'''the function(object) passed should be callable your input : {function}'''
 assert isinstance(_lowerCamelCase			,   (float, int)				), f'''a should be float or integer your input : {a}'''
 assert isinstance(function(_lowerCamelCase				)			,   (float, int)				), (
     "the function should return integer or float return type of your function, "
     f'''{type(_lowerCamelCase				)}'''
 )
 assert isinstance(_lowerCamelCase			,   (float, int)				), f'''b should be float or integer your input : {b}'''
 assert (
     isinstance(_lowerCamelCase			,   _lowerCamelCase				) 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

 lowerCamelCase__     :			int     				=						(b - a) / N_STEPS
 lowerCamelCase__     :			Optional[int]     				=						function(_lowerCamelCase				) + function(_lowerCamelCase				)

 for i in range(1			,   _lowerCamelCase				):
  lowerCamelCase__     :			Union[str, Any]     				=						a + h * i
  result += function(_lowerCamelCase				) * (4 if i % 2 else 2)

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_			: Dict				=			{"configuration_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			: Tuple				=			[
          "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "QDQBertForMaskedLM",
          "QDQBertForMultipleChoice",
          "QDQBertForNextSentencePrediction",
          "QDQBertForQuestionAnswering",
          "QDQBertForSequenceClassification",
          "QDQBertForTokenClassification",
          "QDQBertLayer",
          "QDQBertLMHeadModel",
          "QDQBertModel",
          "QDQBertPreTrainedModel",
          "load_tf_weights_in_qdqbert",
      ]


if TYPE_CHECKING:
      from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_qdqbert import (
                QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                QDQBertForMaskedLM,
                QDQBertForMultipleChoice,
                QDQBertForNextSentencePrediction,
                QDQBertForQuestionAnswering,
                QDQBertForSequenceClassification,
                QDQBertForTokenClassification,
                QDQBertLayer,
                QDQBertLMHeadModel,
                QDQBertModel,
                QDQBertPreTrainedModel,
                load_tf_weights_in_qdqbert,
            )


else:
      import sys

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

