



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__		:					List[str]							=TaTokenizer

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

lowerCAmelCase__		:					Optional[Any]							=TaTokenizerFast

lowerCAmelCase__		:					List[str]							={'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

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

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

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[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

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











from __future__ import annotations

from collections.abc import Generator






def      __lowercase       (			)    ->			Generator[int, None, None]:
							__SCREAMING_SNAKE_CASE									= {}
							__SCREAMING_SNAKE_CASE									= 2
							while True:
														__SCREAMING_SNAKE_CASE									= factor_map.pop(a__							,       a__       )
														if factor:
																					__SCREAMING_SNAKE_CASE									= factor + prime
																					while x in factor_map:
																												x += factor
																					__SCREAMING_SNAKE_CASE									= factor
														else:
																					__SCREAMING_SNAKE_CASE									= prime
																					yield prime
														prime += 1






def      __lowercase       (			a__ = 1E10       )    ->			int:
							__SCREAMING_SNAKE_CASE									= sieve()

							__SCREAMING_SNAKE_CASE									= 1
							while True:
														__SCREAMING_SNAKE_CASE									= next(a__       )
														if (2 * prime * n) > limit:
																					return n
														# Ignore the next prime as the reminder will be 2.
														next(a__       )
														n += 2


if __name__ == "__main__":
			print(solution())






