




'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable


def 					UpperCamelCase_(     snake_case							:	Callable[[int | float], int | float]							,    snake_case							:	int | float							,    snake_case							:	int | float							,    snake_case							:	int = 1_0_0							,    ):

							'''simple docstring'''






							snake_case_      =				x_start
							snake_case_      =				fnc(snake_case					)
							snake_case_      =				0.0
							for _ in range(snake_case					):
														# Approximates small segments of curve as linear and solve
														# for trapezoidal area
														snake_case_      =				(x_end - x_start) / steps + xa
														snake_case_      =				fnc(snake_case					)
														area += abs(fxa + fxa					) * (xa - xa) / 2
														# Increment step
														snake_case_      =				xa
														snake_case_      =				fxa
							return area


if __name__ == "__main__":


		def 					UpperCamelCase_(     snake_case							:	Union[str, Any]					):

									'''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:")
		_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			10
		while i <= 10_0000:
				print(F"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
				i *= 10










'''simple docstring'''





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:       Dict						=			[
		    "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CanineForMultipleChoice",
		    "CanineForQuestionAnswering",
		    "CanineForSequenceClassification",
		    "CanineForTokenClassification",
		    "CanineLayer",
		    "CanineModel",
		    "CaninePreTrainedModel",
		    "load_tf_weights_in_canine",
		]


if TYPE_CHECKING:
		from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
		from .tokenization_canine import CanineTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_canine import (
				    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CanineForMultipleChoice,
				    CanineForQuestionAnswering,
				    CanineForSequenceClassification,
				    CanineForTokenClassification,
				    CanineLayer,
				    CanineModel,
				    CaninePreTrainedModel,
				    load_tf_weights_in_canine,
				)


else:
		import sys

		_SCREAMING_SNAKE_CASE							:       int						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




