



"""simple docstring"""
from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
__a     =    TypeVar("T", bound="Enum")
@unique
class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      List[str]						=					8_0
					_A		:      Optional[int]						=					7_0
					_A		:      List[str]						=					6_0
					_A		:      List[str]						=					5_0
					_A		:      str						=					4_0
					_A		:      Dict						=					3_0
					_A		:      List[Any]						=					2_0
					_A		:      Any						=					1_0

@unique
class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      Tuple						=					2_4
					_A		:      Optional[Any]						=					2_1
					_A		:      Optional[Any]						=					1_8
					_A		:      str						=					1_5
					_A		:      Optional[Any]						=					1_2
					_A		:      Union[str, Any]						=					9
					_A		:      int						=					6
					_A		:      List[str]						=					3
					_A		:      Tuple						=					2
					_A		:      Optional[Any]						=					1
					_A		:      List[Any]						=					-1
					_A		:      Optional[int]						=					-2
					_A		:      Optional[Any]						=					-3
					_A		:      int						=					-6
					_A		:      List[str]						=					-9
					_A		:      Union[str, Any]						=					-1_2
					_A		:      List[Any]						=					-1_5
					_A		:      List[str]						=					-1_8
					_A		:      Dict						=					-2_1
					_A		:      Tuple						=					-2_4






					@classmethod
					def        lowerCAmelCase_    (	cls:							type[T]						)       ->     dict:
											return {unit.name: unit.value for unit in cls if unit.value > 0}






					@classmethod
					def        lowerCAmelCase_    (	cls:							type[T]						)       ->     dict:
											return {unit.name: unit.value for unit in cls if unit.value < 0}

def        A_  (      _lowercase     ):







						'''simple docstring'''


						snake_case_			:List[Any]						=      SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
						for name_prefix, value_prefix in prefixes.items():
												snake_case_			:str						=      value / (10**value_prefix)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {name_prefix}"""
						return str(_lowercase     )
def        A_  (      _lowercase     ):







						'''simple docstring'''


						for prefix in BinaryUnit:
												snake_case_			:int						=      value / (2**prefix.value)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {prefix.name}"""
						return str(_lowercase     )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__a     =    {
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a     =    ["XLMProphetNetTokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a     =    [
						    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "XLMProphetNetDecoder",
						    "XLMProphetNetEncoder",
						    "XLMProphetNetForCausalLM",
						    "XLMProphetNetForConditionalGeneration",
						    "XLMProphetNetModel",
						    "XLMProphetNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_prophetnet import (
												    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMProphetNetDecoder,
												    XLMProphetNetEncoder,
												    XLMProphetNetForCausalLM,
												    XLMProphetNetForConditionalGeneration,
												    XLMProphetNetModel,
												    XLMProphetNetPreTrainedModel,
												)

else:
						import sys

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