

'''simple docstring'''

import math

def     __magic_name__       ( A							)			->		float:
	snake_case							 =				math.log(2					,   1_0							) * number
	snake_case							 =				round((log_number - int(__lowerCamelCase							))					,   1_5							)

	return difference

def     __magic_name__       ( A = 6_7_8_9_1_0							)			->		int:
	snake_case							 =				9_0
	snake_case							 =				0

	snake_case							 =				math.log(1.23					,   1_0							)
	snake_case							 =				math.log(1.24					,   1_0							)
	snake_case							 =				0

	while position < number:
		snake_case							 =				log_difference(__lowerCamelCase							)

		if difference >= upper_limit:
			power_iterator += 9_3

		elif difference < lower_limit:
			power_iterator += 1_9_6

		else:
			snake_case							 =				power_iterator
			power_iterator += 1_9_6
			position += 1

	return previous_power


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						print(f"{solution() = }")








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_						   =			{
    "configuration_led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig"],
    "tokenization_led": ["LEDTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["LEDTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			[
						    "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LEDForConditionalGeneration",
						    "LEDForQuestionAnswering",
						    "LEDForSequenceClassification",
						    "LEDModel",
						    "LEDPreTrainedModel",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]


if TYPE_CHECKING:
						from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
						from .tokenization_led import LEDTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_led_fast import LEDTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_led import (
												    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LEDForConditionalGeneration,
												    LEDForQuestionAnswering,
												    LEDForSequenceClassification,
												    LEDModel,
												    LEDPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
						import sys

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





