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

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


SCREAMING_SNAKE_CASE__   =					{"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

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

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLNetForMultipleChoice",
					    "XLNetForQuestionAnswering",
					    "XLNetForQuestionAnsweringSimple",
					    "XLNetForSequenceClassification",
					    "XLNetForTokenClassification",
					    "XLNetLMHeadModel",
					    "XLNetModel",
					    "XLNetPreTrainedModel",
					    "load_tf_weights_in_xlnet",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLNetForMultipleChoice",
					    "TFXLNetForQuestionAnsweringSimple",
					    "TFXLNetForSequenceClassification",
					    "TFXLNetForTokenClassification",
					    "TFXLNetLMHeadModel",
					    "TFXLNetMainLayer",
					    "TFXLNetModel",
					    "TFXLNetPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlnet import XLNetTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlnet_fast import XLNetTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlnet import (
										    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLNetForMultipleChoice,
										    XLNetForQuestionAnswering,
										    XLNetForQuestionAnsweringSimple,
										    XLNetForSequenceClassification,
										    XLNetForTokenClassification,
										    XLNetLMHeadModel,
										    XLNetModel,
										    XLNetPreTrainedModel,
										    load_tf_weights_in_xlnet,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlnet import (
										    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLNetForMultipleChoice,
										    TFXLNetForQuestionAnsweringSimple,
										    TFXLNetForSequenceClassification,
										    TFXLNetForTokenClassification,
										    TFXLNetLMHeadModel,
										    TFXLNetMainLayer,
										    TFXLNetModel,
										    TFXLNetPreTrainedModel,
										)

else:
					import sys

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



"""simple docstring"""
import cmath
import math





def 				lowerCAmelCase__      (     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float   )   ->			complex:





						"""simple docstring"""






						snake_case        =     math.radians(_UpperCamelCase   )
						snake_case        =     math.radians(_UpperCamelCase   )

						# Convert voltage and current to rectangular form
						snake_case        =     cmath.rect(_UpperCamelCase	,     _UpperCamelCase   )
						snake_case        =     cmath.rect(_UpperCamelCase	,     _UpperCamelCase   )

						# Calculate apparent power
						return voltage_rect * current_rect


if __name__ == "__main__":
					import doctest

					doctest.testmod()


