
"""simple docstring"""






from typing import TYPE_CHECKING

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


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

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

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


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase          =							["""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

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




"""simple docstring"""






def __magic_name__  (							_lowerCamelCase:		list[int] )				->	int:

	'''simple docstring'''




	lowerCAmelCase				       =     len(_lowerCamelCase )
	if n in (0, 1):
		return 0
	return sum(query * (n - i - 1) for i, query in enumerate(sorted(_lowerCamelCase ) ) )


if __name__ == "__main__":
					import doctest

					doctest.testmod()


