





"""simple docstring"""






from __future__ import annotations

import math
from collections.abc import Callable

def     __A      (			a_						:Callable[[int | float], int | float]   ,       a_						:int | float   ,       a_						:int | float   ,       a_						:int = 1_00   ,       )		->		float:
				__a :						Optional[Any]			       =  x_start
				__a :						List[Any]			       =  fnc(a_)
				__a :						Any			       =  0.0

				for _ in range(a_):
								# Approximates curve as a sequence of linear lines and sums their length
								__a :						str			       =  (x_end - x_start) / steps + xa
								__a :						List[Any]			       =  fnc(a_)
								length += math.hypot(xa - xa   ,       fxa - fxa)

								# Increment step
								__a :						List[Any]			       =  xa
								__a :						Tuple			       =  fxa

				return length


if __name__ == "__main__":

		def     __A      (			a_						:Optional[Any])		->		Dict:
						return math.sin(10 * x)

		print('''f(x) = sin(10 * x)''')
		print('''The length of the curve from x = -10 to x = 10 is:''')
		A 			=  10
		while i <= 100_000:
				print(F'With {i} steps: {line_length(f, -10, 10, i)}')
				i *= 10






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A 			=  {
    '''configuration_jukebox''': [
        '''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''JukeboxConfig''',
        '''JukeboxPriorConfig''',
        '''JukeboxVQVAEConfig''',
    ],
    '''tokenization_jukebox''': ['''JukeboxTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A 			=  [
		    '''JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''JukeboxModel''',
		    '''JukeboxPreTrainedModel''',
		    '''JukeboxVQVAE''',
		    '''JukeboxPrior''',
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

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