





'''simple docstring'''




from __future__ import annotations

import math
from collections.abc import Callable



def 						__A						(   lowerCamelCase_       ,       lowerCamelCase_       ,       lowerCamelCase_       ,       lowerCamelCase_ = 1_00       ,       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE      :							List[str]						=       x_start
		SCREAMING_SNAKE_CASE      :							List[str]						=       fnc(__a     )
		SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       0.0

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

				# Increment step
				SCREAMING_SNAKE_CASE      :							Optional[Any]						=       xa
				SCREAMING_SNAKE_CASE      :							List[str]						=       fxa

		return length


if __name__ == "__main__":



							def 						__A						(   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:""")
							__UpperCAmelCase			    =    10
							while i <= 100000:
														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


__A									=				{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "IBertForMaskedLM",
         "IBertForMultipleChoice",
         "IBertForQuestionAnswering",
         "IBertForSequenceClassification",
         "IBertForTokenClassification",
         "IBertModel",
         "IBertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ibert import (
              IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              IBertForMaskedLM,
              IBertForMultipleChoice,
              IBertForQuestionAnswering,
              IBertForSequenceClassification,
              IBertForTokenClassification,
              IBertModel,
              IBertPreTrainedModel,
          )

else:
     import sys

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

