
'''simple docstring'''

from __future__ import annotations

from collections.abc import Callable


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    Optional[Any]					, _lowerCamelCase  :    Dict					, _lowerCamelCase  :    int = 1_00					, )      ->  Optional[Any]:
  _lowerCAmelCase    :					int							=   x_start
  _lowerCAmelCase    :					List[Any]							=   fnc(__A						)
  _lowerCAmelCase    :					List[Any]							=   0.0

  for _ in range(__A						):
    # Approximates small segments of curve as linear and solve
    # for trapezoidal area
    _lowerCAmelCase    :					List[Any]							=   (x_end - x_start) / steps + xa
    _lowerCAmelCase    :					Optional[Any]							=   fnc(__A						)
    area += abs(fxa + fxa						) * (xa - xa) / 2

    # Increment step
    _lowerCAmelCase    :					Any							=   xa
    _lowerCAmelCase    :					Optional[int]							=   fxa
  return area


if __name__ == "__main__":


       def 		_UpperCAmelCase			(					_lowerCamelCase  :    Tuple						)      ->  Union[str, Any]:
         return x**3

       print("""f(x) = x^3""")
       print("""The area between the curve, x = -10, x = 10 and the x axis is:""")
       UpperCamelCase_            =    10
       while i <= 10_00_00:
              UpperCamelCase_            =    trapezoidal_area(f, -5, 5, i)
              print(F'with {i} steps: {area}')
              i *= 10




'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCamelCase_            =    {"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GPTJForCausalLM""",
         """GPTJForQuestionAnswering""",
         """GPTJForSequenceClassification""",
         """GPTJModel""",
         """GPTJPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """TFGPTJForCausalLM""",
         """TFGPTJForQuestionAnswering""",
         """TFGPTJForSequenceClassification""",
         """TFGPTJModel""",
         """TFGPTJPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """FlaxGPTJForCausalLM""",
         """FlaxGPTJModel""",
         """FlaxGPTJPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

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


