



from __future__ import annotations

from collections.abc import Callable



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase = 100  ,  )   ->					float:


				"""simple docstring"""

				snake_case__	:      str     		=	x_start
				snake_case__	:      List[Any]     		=	fnc(__lowerCAmelCase      )
				snake_case__	:      List[Any]     		=	0.0
				for _ in range(__lowerCAmelCase      ):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								snake_case__	:      Optional[Any]     		=	(x_end - x_start) / steps + xa
								snake_case__	:      List[str]     		=	fnc(__lowerCAmelCase      )
								area += abs(fxa + fxa      ) * (xa - xa) / 2
								# Increment step
								snake_case__	:      str     		=	xa
								snake_case__	:      int     		=	fxa
				return area


if __name__ == "__main__":



				def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


								"""simple docstring"""

								return x**3 + x**2

				print('''f(x) = x^3 + x^2''')
				print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
				A__								=						10
				while i <= 10_0000:
								print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
								i *= 10






from typing import TYPE_CHECKING

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


A__								=						{
    '''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
    '''tokenization_ctrl''': ['''CTRLTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A__								=						[
				    '''CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''CTRLForSequenceClassification''',
				    '''CTRLLMHeadModel''',
				    '''CTRLModel''',
				    '''CTRLPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A__								=						[
				    '''TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFCTRLForSequenceClassification''',
				    '''TFCTRLLMHeadModel''',
				    '''TFCTRLModel''',
				    '''TFCTRLPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
				from .tokenization_ctrl import CTRLTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ctrl import (
								    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CTRLForSequenceClassification,
								    CTRLLMHeadModel,
								    CTRLModel,
								    CTRLPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ctrl import (
								    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFCTRLForSequenceClassification,
								    TFCTRLLMHeadModel,
								    TFCTRLModel,
								    TFCTRLPreTrainedModel,
								)

else:
				import sys

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

