




from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowercase__    :					Optional[int]					=						TypeVar("T", bound="Enum")
@unique
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       80
						lowerCAmelCase_							=       70
						lowerCAmelCase_							=       60
						lowerCAmelCase_							=       50
						lowerCAmelCase_							=       40
						lowerCAmelCase_							=       30
						lowerCAmelCase_							=       20
						lowerCAmelCase_							=       10







@unique
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       24
						lowerCAmelCase_							=       21
						lowerCAmelCase_							=       18
						lowerCAmelCase_							=       15
						lowerCAmelCase_							=       12
						lowerCAmelCase_							=       9
						lowerCAmelCase_							=       6
						lowerCAmelCase_							=       3
						lowerCAmelCase_							=       2
						lowerCAmelCase_							=       1
						lowerCAmelCase_							=       -1
						lowerCAmelCase_							=       -2
						lowerCAmelCase_							=       -3
						lowerCAmelCase_							=       -6
						lowerCAmelCase_							=       -9
						lowerCAmelCase_							=       -12
						lowerCAmelCase_							=       -15
						lowerCAmelCase_							=       -18
						lowerCAmelCase_							=       -21
						lowerCAmelCase_							=       -24






						@classmethod
						def 	snake_case__						(						cls							:					type[T]						):






							"""simple docstring"""






							return {unit.name: unit.value for unit in cls if unit.value > 0}






						@classmethod
						def 	snake_case__						(						cls							:					type[T]						):






							"""simple docstring"""






							return {unit.name: unit.value for unit in cls if unit.value < 0}







def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





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


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





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


if __name__ == "__main__":
		import doctest

		doctest.testmod()











from typing import TYPE_CHECKING

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


lowercase__    :					List[str]					=						{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					List[Any]					=						[
		    "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

		lowercase__    :					Any					=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
