





def _a							(      lowerCamelCase:  int   ,							lowerCamelCase:  Optional[int]					)      ->		List[str]:







      '''simple docstring'''






      __A			  =							(boundary[1] - boundary[0]) / steps
      __A			  =							boundary[0]
      __A			  =							boundary[1]
      __A			  =							make_points(lowerCamelCase   ,							lowerCamelCase   ,							lowerCamelCase					)
      __A			  =							0.0
      y += (h / 3.0) * f(lowerCamelCase					)
      __A			  =							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 _a							(      lowerCamelCase:  List[Any]   ,							lowerCamelCase:  Optional[Any]   ,							lowerCamelCase:  str					)      ->		str:







      '''simple docstring'''






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



def _a							(      lowerCamelCase:  Dict					)      ->		List[Any]:  # enter your function here







      '''simple docstring'''






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



def _a							(      )      ->		Tuple:







      '''simple docstring'''






      __A			  =							0.0  # Lower bound of integration
      __A			  =							1.0  # Upper bound of integration
      __A			  =							10.0  # define number of steps or resolution
      __A			  =							[a, b]  # define boundary of integration
      __A			  =							method_a(lowerCamelCase   ,							lowerCamelCase					)
      print(F"""y = {y}"""					)


if __name__ == "__main__":
       main()












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__   :				Any						=	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				List[Any]						=	['CamembertTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Dict						=	['CamembertTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CamembertForCausalLM',
           'CamembertForMaskedLM',
           'CamembertForMultipleChoice',
           'CamembertForQuestionAnswering',
           'CamembertForSequenceClassification',
           'CamembertForTokenClassification',
           'CamembertModel',
           'CamembertPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFCamembertForCausalLM',
           'TFCamembertForMaskedLM',
           'TFCamembertForMultipleChoice',
           'TFCamembertForQuestionAnswering',
           'TFCamembertForSequenceClassification',
           'TFCamembertForTokenClassification',
           'TFCamembertModel',
           'TFCamembertPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

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





