



"""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__		:				Any          =	TaTokenizer

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

lowercase__		:				Any          =	TaTokenizerFast

lowercase__		:				Any          =	{"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

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

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				List[Any]          =	["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				str          =	["""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__		:				List[str]          =	_LazyModule(
         __name__,
         globals()["""__file__"""],
         _import_structure,
         extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
         module_spec=__spec__,
     )










"""simple docstring"""







def 				UpperCamelCase_    (    lowerCAmelCase__      :				int = 100						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= (n * (n + 1) // 2) ** 2
  lowerCAmelCase_   :				Optional[int]								= n * (n + 1) * (2 * n + 1) // 6
  return sum_cubes - sum_squares


if __name__ == "__main__":
     print(f'{solution() = }')





