





'''simple docstring'''







from typing import TYPE_CHECKING

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


snake_case_  :							Any            =	{
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							List[Any]            =	["MPNetTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Union[str, Any]            =	[
					    "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "MPNetForMaskedLM",
					    "MPNetForMultipleChoice",
					    "MPNetForQuestionAnswering",
					    "MPNetForSequenceClassification",
					    "MPNetForTokenClassification",
					    "MPNetLayer",
					    "MPNetModel",
					    "MPNetPreTrainedModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							List[str]            =	[
					    "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFMPNetEmbeddings",
					    "TFMPNetForMaskedLM",
					    "TFMPNetForMultipleChoice",
					    "TFMPNetForQuestionAnswering",
					    "TFMPNetForSequenceClassification",
					    "TFMPNetForTokenClassification",
					    "TFMPNetMainLayer",
					    "TFMPNetModel",
					    "TFMPNetPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
					from .tokenization_mpnet import MPNetTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mpnet_fast import MPNetTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mpnet import (
										    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MPNetForMaskedLM,
										    MPNetForMultipleChoice,
										    MPNetForQuestionAnswering,
										    MPNetForSequenceClassification,
										    MPNetForTokenClassification,
										    MPNetLayer,
										    MPNetModel,
										    MPNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mpnet import (
										    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFMPNetEmbeddings,
										    TFMPNetForMaskedLM,
										    TFMPNetForMultipleChoice,
										    TFMPNetForQuestionAnswering,
										    TFMPNetForSequenceClassification,
										    TFMPNetForTokenClassification,
										    TFMPNetMainLayer,
										    TFMPNetModel,
										    TFMPNetPreTrainedModel,
										)

else:
					import sys

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






"""simple docstring"""


from doctest import testmod
from math import sqrt

def 						UpperCamelCase				(	_lowerCAmelCase   :							int						):
    __a			      =						[]
    if num < 1:
        return facs
    facs.append(1						)
    if num == 1:
        return facs
    facs.append(_lowerCAmelCase						)
    for i in range(2				, int(sqrt(_lowerCAmelCase						)						) + 1						):
        if num % i == 0:  # If i is a factor of num
            facs.append(_lowerCAmelCase						)
            __a			      =						num // i  # num//i is the other factor of num
            if d != i:  # If d and i are distinct
                facs.append(_lowerCAmelCase						)  # we have found another factor
    facs.sort()
    return facs


if __name__ == "__main__":
 testmod(name="""factors_of_a_number""", verbose=True)
