


'''simple docstring'''



from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
	from ..ta.tokenization_ta import TaTokenizer
else:
	from ...utils.dummy_sentencepiece_objects import TaTokenizer

__lowercase	:						Union[str, Any]       =	TaTokenizer

if is_tokenizers_available():
	from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
	from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__lowercase	:						int       =	TaTokenizerFast

__lowercase	:						int       =	{'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						int       =	[
	    'MT5EncoderModel',
	    'MT5ForConditionalGeneration',
	    'MT5ForQuestionAnswering',
	    'MT5Model',
	    'MT5PreTrainedModel',
	    'MT5Stack',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[str]       =	['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Any       =	['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
	from .configuration_mta import MTaConfig, MTaOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mta import (
		    MTaEncoderModel,
		    MTaForConditionalGeneration,
		    MTaForQuestionAnswering,
		    MTaModel,
		    MTaPreTrainedModel,
		    MTaStack,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
	import sys

	__lowercase	:						Optional[int]       =	_LazyModule(
	    __name__,
	    globals()['__file__'],
	    _import_structure,
	    extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
	    module_spec=__spec__,
	)








import cmath
import math






def     a(      A       :    float ,     A       :    float ,     A       :    float ,     A       :    float       )		->       complex:
					"""simple docstring"""





					a					     =   math.radians(A       )
					a					     =   math.radians(A       )

					# Convert voltage and current to rectangular form
					a					     =   cmath.rect(A ,     A       )
					a					     =   cmath.rect(A ,     A       )

					# Calculate apparent power
					return voltage_rect * current_rect


if __name__ == "__main__":
				import doctest

				doctest.testmod()
