
from __future__ import annotations

import math
from collections.abc import Callable



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__ = 100			,      ):
					'''simple docstring'''
					lowercase						     =						x_start
					lowercase						     =						fnc(snake_case__		)
					lowercase						     =						0.0

					for _ in range(snake_case__		):
										# Approximates curve as a sequence of linear lines and sums their length
										lowercase						     =						(x_end - x_start) / steps + xa
										lowercase						     =						fnc(snake_case__		)
										length += math.hypot(xa - xa			,      fxa - fxa		)

										# Increment step
										lowercase						     =						xa
										lowercase						     =						fxa

					return length


if __name__ == "__main__":



		def 	UpperCamelCase   (							lowerCAmelCase__		):
							'''simple docstring'''
							return math.sin(10 * x		)

		print("f(x) = sin(10 * x)")
		print("The length of the curve from x = -10 to x = 10 is:")
		lowercase__		:Dict			  =   10
		while i <= 10_0000:
				print(F'With {i} steps: {line_length(f, -10, 10, i)}')
				i *= 10






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__		:Dict			  =   {
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:List[str]			  =   [
							    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "LukeForEntityClassification",
							    "LukeForEntityPairClassification",
							    "LukeForEntitySpanClassification",
							    "LukeForMultipleChoice",
							    "LukeForQuestionAnswering",
							    "LukeForSequenceClassification",
							    "LukeForTokenClassification",
							    "LukeForMaskedLM",
							    "LukeModel",
							    "LukePreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
							from .tokenization_luke import LukeTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_luke import (
														    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LukeForEntityClassification,
														    LukeForEntityPairClassification,
														    LukeForEntitySpanClassification,
														    LukeForMaskedLM,
														    LukeForMultipleChoice,
														    LukeForQuestionAnswering,
														    LukeForSequenceClassification,
														    LukeForTokenClassification,
														    LukeModel,
														    LukePreTrainedModel,
														)

else:
							import sys

							lowercase__		:Tuple			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




