



def 						_lowerCAmelCase    (					__lowerCamelCase :  List[Any]		,						__lowerCamelCase :  List[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[str]							=						(boundary[1] - boundary[0]) / steps
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						boundary[0]
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						boundary[1]
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						make_points(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     str							=						0.0
 y += (h / 3.0) * f(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     str							=						2
 for i in x_i:
  y += (h / 3) * (4 - 2 * (cnt % 2)) * f(__lowerCamelCase  )
  cnt += 1
 y += (h / 3.0) * f(__lowerCamelCase  )
 return y






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[str]		,						__lowerCamelCase :  Union[str, Any]		,						__lowerCamelCase :  Any  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[str]							=						a + h
 while x < (b - h):
  yield x
  __SCREAMING_SNAKE_CASE							:     Any							=						x + h






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[str]  ):  # enter your function here



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						(x - 0) * (x - 0)
 return y






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						0.0  # Lower bound of integration
 __SCREAMING_SNAKE_CASE							:     List[str]							=						1.0  # Upper bound of integration
 __SCREAMING_SNAKE_CASE							:     str							=						10.0  # define number of steps or resolution
 __SCREAMING_SNAKE_CASE							:     Dict							=						[a, b]  # define boundary of integration
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						method_a(__lowerCamelCase		,						__lowerCamelCase  )
 print(F"""y = {y}"""  )


if __name__ == "__main__":
      main()










'''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     :Union[str, Any]												=  {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Union[str, Any]												=  [
          "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     :Any												=  [
          "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     :Optional[Any]												=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)