"""simple docstring"""







from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE  :		Optional[int]        =      {
    '''configuration_big_bird''': ['''BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BigBirdConfig''', '''BigBirdOnnxConfig'''],
}

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

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE  :		Optional[Any]        =      ['''BigBirdTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE  :		List[str]        =      [
         '''BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''BigBirdForCausalLM''',
         '''BigBirdForMaskedLM''',
         '''BigBirdForMultipleChoice''',
         '''BigBirdForPreTraining''',
         '''BigBirdForQuestionAnswering''',
         '''BigBirdForSequenceClassification''',
         '''BigBirdForTokenClassification''',
         '''BigBirdLayer''',
         '''BigBirdModel''',
         '''BigBirdPreTrainedModel''',
         '''load_tf_weights_in_big_bird''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE  :		List[str]        =      [
         '''FlaxBigBirdForCausalLM''',
         '''FlaxBigBirdForMaskedLM''',
         '''FlaxBigBirdForMultipleChoice''',
         '''FlaxBigBirdForPreTraining''',
         '''FlaxBigBirdForQuestionAnswering''',
         '''FlaxBigBirdForSequenceClassification''',
         '''FlaxBigBirdForTokenClassification''',
         '''FlaxBigBirdModel''',
         '''FlaxBigBirdPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_big_bird import BigBirdTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_big_bird_fast import BigBirdTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_big_bird import (
              BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
              BigBirdForCausalLM,
              BigBirdForMaskedLM,
              BigBirdForMultipleChoice,
              BigBirdForPreTraining,
              BigBirdForQuestionAnswering,
              BigBirdForSequenceClassification,
              BigBirdForTokenClassification,
              BigBirdLayer,
              BigBirdModel,
              BigBirdPreTrainedModel,
              load_tf_weights_in_big_bird,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_big_bird import (
              FlaxBigBirdForCausalLM,
              FlaxBigBirdForMaskedLM,
              FlaxBigBirdForMultipleChoice,
              FlaxBigBirdForPreTraining,
              FlaxBigBirdForQuestionAnswering,
              FlaxBigBirdForSequenceClassification,
              FlaxBigBirdForTokenClassification,
              FlaxBigBirdModel,
              FlaxBigBirdPreTrainedModel,
          )

else:
     import sys

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







"""simple docstring"""







from __future__ import annotations

from collections.abc import Callable



def 			A__						(  lowercase_    :  Callable[[int | float], int | float]			,   lowercase_    :  int | float			,   lowercase_    :  int | float			,   lowercase_    :  int = 100			,   )     ->       float:
   __lowerCamelCase								=				x_start
   __lowerCamelCase								=				fnc(lowercase_	)
   __lowerCamelCase								=				0.0

   for _ in range(lowercase_	):
      # Approximates small segments of curve as linear and solve
      # for trapezoidal area
      __lowerCamelCase								=				(x_end - x_start) / steps + xa
      __lowerCamelCase								=				fnc(lowercase_	)
      area += abs(fxa + fxa	) * (xa - xa) / 2

      # Increment step
      __lowerCamelCase								=				xa
      __lowerCamelCase								=				fxa
   return area


if __name__ == "__main__":



     def 			A__						(  lowercase_    :  Union[str, Any]	)     ->       int:
        return x**3

     print('''f(x) = x^3''')
     print('''The area between the curve, x = -10, x = 10 and the x axis is:''')
     _SCREAMING_SNAKE_CASE  :		Any        =      1_0
     while i <= 1_0_0_0_0_0:
          _SCREAMING_SNAKE_CASE  :		List[str]        =      trapezoidal_area(f, -5, 5, i)
          print(F"with {i} steps: {area}")
          i *= 1_0






