
"""simple docstring"""

def      lowercase__    (       lowerCAmelCase					:       List[str]			,					lowerCAmelCase					:       Optional[Any]			)			->   Tuple:






    """simple docstring"""




    UpperCAmelCase          =       (boundary[1] - boundary[0]) / steps
    UpperCAmelCase          =       boundary[0]
    UpperCAmelCase          =       boundary[1]
    UpperCAmelCase          =       make_points(lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			)
    UpperCAmelCase          =       0.0
    y += (h / 2.0) * f(lowerCAmelCase			)
    for i in x_i:
        # print(i)
        y += h * f(lowerCAmelCase			)
    y += (h / 2.0) * f(lowerCAmelCase			)
    return y




def      lowercase__    (       lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       List[Any]			)			->   Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase          =       a + h
    while x < (b - h):
        yield x
        UpperCAmelCase          =       x + h




def      lowercase__    (       lowerCAmelCase					:       List[str]			)			->   Tuple:  # enter your function here






    """simple docstring"""




    UpperCAmelCase          =       (x - 0) * (x - 0)
    return y




def      lowercase__    (       )			->   Union[str, Any]:






    """simple docstring"""




    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(lowerCAmelCase			,					lowerCAmelCase			)
    print(F"y = {y}"			)


if __name__ == "__main__":
   main()





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_            =    {
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



