

'''simple docstring'''




from typing import TYPE_CHECKING

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


_a					:			Optional[Any]							=    {
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			str							=    [
      'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'RobertaPreLayerNormForCausalLM',
      'RobertaPreLayerNormForMaskedLM',
      'RobertaPreLayerNormForMultipleChoice',
      'RobertaPreLayerNormForQuestionAnswering',
      'RobertaPreLayerNormForSequenceClassification',
      'RobertaPreLayerNormForTokenClassification',
      'RobertaPreLayerNormModel',
      'RobertaPreLayerNormPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Any							=    [
      'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFRobertaPreLayerNormForCausalLM',
      'TFRobertaPreLayerNormForMaskedLM',
      'TFRobertaPreLayerNormForMultipleChoice',
      'TFRobertaPreLayerNormForQuestionAnswering',
      'TFRobertaPreLayerNormForSequenceClassification',
      'TFRobertaPreLayerNormForTokenClassification',
      'TFRobertaPreLayerNormMainLayer',
      'TFRobertaPreLayerNormModel',
      'TFRobertaPreLayerNormPreTrainedModel',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Tuple							=    [
      'FlaxRobertaPreLayerNormForCausalLM',
      'FlaxRobertaPreLayerNormForMaskedLM',
      'FlaxRobertaPreLayerNormForMultipleChoice',
      'FlaxRobertaPreLayerNormForQuestionAnswering',
      'FlaxRobertaPreLayerNormForSequenceClassification',
      'FlaxRobertaPreLayerNormForTokenClassification',
      'FlaxRobertaPreLayerNormModel',
      'FlaxRobertaPreLayerNormPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
      RobertaPreLayerNormConfig,
      RobertaPreLayerNormOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        RobertaPreLayerNormForCausalLM,
        RobertaPreLayerNormForMaskedLM,
        RobertaPreLayerNormForMultipleChoice,
        RobertaPreLayerNormForQuestionAnswering,
        RobertaPreLayerNormForSequenceClassification,
        RobertaPreLayerNormForTokenClassification,
        RobertaPreLayerNormModel,
        RobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roberta_prelayernorm import (
        TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRobertaPreLayerNormForCausalLM,
        TFRobertaPreLayerNormForMaskedLM,
        TFRobertaPreLayerNormForMultipleChoice,
        TFRobertaPreLayerNormForQuestionAnswering,
        TFRobertaPreLayerNormForSequenceClassification,
        TFRobertaPreLayerNormForTokenClassification,
        TFRobertaPreLayerNormMainLayer,
        TFRobertaPreLayerNormModel,
        TFRobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
        FlaxRobertaPreLayerNormPreTrainedModel,
    )

else:
  import sys

  _a					:			List[Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





'''simple docstring'''




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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : Tuple    ):
  UpperCAmelCase    				=						a + h
  while x < (b - h):
    yield x
    UpperCAmelCase    				=						x + h


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]    ):  # enter your function here
  UpperCAmelCase    				=						(x - 0) * (x - 0)
  return y


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


if __name__ == "__main__":
  main()


