"""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

__lowerCamelCase							 =     TaTokenizer

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

__lowerCamelCase							 =     TaTokenizerFast

__lowerCamelCase							 =     {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

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

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

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     ['''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

				__lowerCamelCase							 =     _LazyModule(
				    __name__,
				    globals()["__file__"],
				    _import_structure,
				    extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
				    module_spec=__spec__,
				)










'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   float       ,      _lowerCamelCase:   list[float]					):
		if discount_rate < 0:
				raise ValueError("""Discount rate cannot be negative"""					)
		if not cash_flows:
				raise ValueError("""Cash flows list cannot be empty"""					)
		__SCREAMING_SNAKE_CASE       :		List[str]           =		sum(
		    cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_lowerCamelCase					)					)
		return round(_lowerCamelCase       ,      ndigits=2					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()