
'''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					:							int   =      TaTokenizer

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

lowercase					:							Any   =      TaTokenizerFast

lowercase					:							Optional[int]   =      {"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					:							Optional[Any]   =      ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]

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



'''simple docstring'''







from __future__ import annotations







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	if not nums:
		return 0
	_snake_case     		=	nums[0]
	_snake_case     		=	0
	for num in nums[1:]:
		_snake_case		,				_snake_case     		=	(
		    max_excluding + num,
		    max(__A    ,       __A			),
		)
	return max(__A    ,       __A			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

