





'''simple docstring'''

from typing import TYPE_CHECKING

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


a   	=    {
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a   	=    [
        "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TapasForMaskedLM",
        "TapasForQuestionAnswering",
        "TapasForSequenceClassification",
        "TapasModel",
        "TapasPreTrainedModel",
        "load_tf_weights_in_tapas",
    ]
try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a   	=    [
        "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFTapasForMaskedLM",
        "TFTapasForQuestionAnswering",
        "TFTapasForSequenceClassification",
        "TFTapasModel",
        "TFTapasPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
    from .tokenization_tapas import TapasTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tapas import (
            TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
            TapasForMaskedLM,
            TapasForQuestionAnswering,
            TapasForSequenceClassification,
            TapasModel,
            TapasPreTrainedModel,
            load_tf_weights_in_tapas,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_tapas import (
            TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFTapasForMaskedLM,
            TFTapasForQuestionAnswering,
            TFTapasForSequenceClassification,
            TFTapasModel,
            TFTapasPreTrainedModel,
        )


else:
    import sys

    a   	=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












'''simple docstring'''

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Optional[Any]:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase   )    ->       int:  # enter your function here

   '''simple docstring'''







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

def     __magic_name__							(				)    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							0.0  # Lower bound of integration
   __SCREAMING_SNAKE_CASE   				=							1.0  # Upper bound of integration
   __SCREAMING_SNAKE_CASE   				=							1_0.0  # define number of steps or resolution
   __SCREAMING_SNAKE_CASE   				=							[a, b]  # define boundary of integration
   __SCREAMING_SNAKE_CASE   				=							method_a(__UpperCAmelCase				,						__UpperCAmelCase   )
   print(f"""y = {y}"""   )


if __name__ == "__main__":
    main()





