






'''simple docstring'''





from typing import TYPE_CHECKING

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


lowercase  :							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:
							lowercase  :							Optional[int]			  =    [
							    '''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:
							lowercase  :							Optional[int]			  =    [
							    '''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:
							lowercase  :							str			  =    [
							    '''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

							lowercase  :							str			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			int    ):
		# "extended trapezoidal rule"
		# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
		snake_case_				:							Optional[Any]						 =       (boundary[1] - boundary[0]) / steps
		snake_case_				:							str						 =       boundary[0]
		snake_case_				:							Dict						 =       boundary[1]
		snake_case_				:							str						 =       make_points(_a  ,      _a  ,      _a    )
		snake_case_				:							str						 =       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      lowerCAmelCase__	(    _a       :			str  ,      _a       :			Optional[Any]  ,      _a       :			Tuple    ):
		snake_case_				:							Tuple						 =       a + h
		while x < (b - h):
				yield x
				snake_case_				:							List[str]						 =       x + h

def      lowerCAmelCase__	(    _a       :			Tuple    ):  # enter your function here
		snake_case_				:							Tuple						 =       (x - 0) * (x - 0)
		return y

def      lowerCAmelCase__	(    ):
		snake_case_				:							Union[str, Any]						 =       0.0  # Lower bound of integration
		snake_case_				:							List[Any]						 =       1.0  # Upper bound of integration
		snake_case_				:							Tuple						 =       10.0  # define number of steps or resolution
		snake_case_				:							List[Any]						 =       [a, b]  # define boundary of integration
		snake_case_				:							Any						 =       method_a(_a  ,      _a    )
		print(F'''y = {y}'''    )


if __name__ == "__main__":
							main()
