


import math




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[str]		=[]
       _lowerCamelCase       :		Optional[Any]		=2
       _lowerCamelCase       :		Optional[int]		=int(math.sqrt(SCREAMING_SNAKE_CASE__     )     )  # Size of every segment
       _lowerCamelCase       :		Tuple		=[True] * (end + 1)
       _lowerCamelCase       :		Tuple		=[]

       while start <= end:
              if temp[start] is True:
                     in_prime.append(SCREAMING_SNAKE_CASE__     )
                     for i in range(start * start		,			end + 1		,			SCREAMING_SNAKE_CASE__     ):
                            _lowerCamelCase       :		Optional[Any]		=False
              start += 1
       prime += in_prime

       _lowerCamelCase       :		Union[str, Any]		=end + 1
       _lowerCamelCase       :		Union[str, Any]		=min(2 * end		,			SCREAMING_SNAKE_CASE__     )

       while low <= n:
              _lowerCamelCase       :		List[Any]		=[True] * (high - low + 1)
              for each in in_prime:
                     _lowerCamelCase       :		List[str]		=math.floor(low / each     ) * each
                     if t < low:
                            t += each

                     for j in range(SCREAMING_SNAKE_CASE__		,			high + 1		,			SCREAMING_SNAKE_CASE__     ):
                            _lowerCamelCase       :		Union[str, Any]		=False

              for j in range(len(SCREAMING_SNAKE_CASE__     )     ):
                     if temp[j] is True:
                            prime.append(j + low     )

              _lowerCamelCase       :		List[Any]		=high + 1
              _lowerCamelCase       :		Tuple		=min(high + end		,			SCREAMING_SNAKE_CASE__     )

       return prime


print(sieve(10**6))






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__,
 )


