


'''simple docstring'''




def 				a_							(    lowercase				,    lowercase     )      ->Dict:






				'''simple docstring'''

				__lowerCamelCase		:   Optional[int]           =       (boundary[1] - boundary[0]) / steps
				__lowerCamelCase		:   Union[str, Any]           =       boundary[0]
				__lowerCamelCase		:   int           =       boundary[1]
				__lowerCamelCase		:   str           =       make_points(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__     )
				__lowerCamelCase		:   Tuple           =       0.0
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE__     )
				__lowerCamelCase		:   Optional[Any]           =       2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(SCREAMING_SNAKE_CASE__     )
								cnt += 1
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE__     )
				return y





def 				a_							(    lowercase				,    lowercase				,    lowercase     )      ->Tuple:






				'''simple docstring'''

				__lowerCamelCase		:   Union[str, Any]           =       a + h
				while x < (b - h):
								yield x
								__lowerCamelCase		:   List[Any]           =       x + h





def 				a_							(    lowercase     )      ->Optional[int]:  # enter your function here






				'''simple docstring'''

				__lowerCamelCase		:   Tuple           =       (x - 0) * (x - 0)
				return y





def 				a_							(    )      ->List[str]:






				'''simple docstring'''

				__lowerCamelCase		:   int           =       0.0  # Lower bound of integration
				__lowerCamelCase		:   List[str]           =       1.0  # Upper bound of integration
				__lowerCamelCase		:   str           =       1_0.0  # define number of steps or resolution
				__lowerCamelCase		:   str           =       [a, b]  # define boundary of integration
				__lowerCamelCase		:   List[str]           =       method_a(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__     )
				print(F'y = {y}'     )


if __name__ == "__main__":
				main()









from typing import TYPE_CHECKING

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


_A									=		{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaubertForMultipleChoice',
       'FlaubertForQuestionAnswering',
       'FlaubertForQuestionAnsweringSimple',
       'FlaubertForSequenceClassification',
       'FlaubertForTokenClassification',
       'FlaubertModel',
       'FlaubertWithLMHeadModel',
       'FlaubertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFlaubertForMultipleChoice',
       'TFFlaubertForQuestionAnsweringSimple',
       'TFFlaubertForSequenceClassification',
       'TFFlaubertForTokenClassification',
       'TFFlaubertModel',
       'TFFlaubertPreTrainedModel',
       'TFFlaubertWithLMHeadModel',
   ]


if TYPE_CHECKING:
   from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
   from .tokenization_flaubert import FlaubertTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flaubert import (
          FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaubertForMultipleChoice,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertModel,
          FlaubertPreTrainedModel,
          FlaubertWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_flaubert import (
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFFlaubertForMultipleChoice,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForSequenceClassification,
          TFFlaubertForTokenClassification,
          TFFlaubertModel,
          TFFlaubertPreTrainedModel,
          TFFlaubertWithLMHeadModel,
      )

else:
   import sys

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





