

import math




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						list[int]:



    """simple docstring"""






    UpperCamelCase_			  =						[]
    UpperCamelCase_			  =						2
    UpperCamelCase_			  =						int(math.sqrt(SCREAMING_SNAKE_CASE_			)			)  # Size of every segment
    UpperCamelCase_			  =						[True] * (end + 1)
    UpperCamelCase_			  =						[]

    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_			):
                UpperCamelCase_			  =						False
        start += 1
    prime += in_prime

    UpperCamelCase_			  =						end + 1
    UpperCamelCase_			  =						min(2 * end			,						SCREAMING_SNAKE_CASE_			)

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

            for j in range(SCREAMING_SNAKE_CASE_			,						high + 1			,						SCREAMING_SNAKE_CASE_			):
                UpperCamelCase_			  =						False

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

        UpperCamelCase_			  =						high + 1
        UpperCamelCase_			  =						min(high + end			,						SCREAMING_SNAKE_CASE_			)

    return prime
print(sieve(10**6))



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     ["""SpeechT5Tokenizer"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Any						=     [
     """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SpeechT5ForSpeechToText""",
     """SpeechT5ForSpeechToSpeech""",
     """SpeechT5ForTextToSpeech""",
     """SpeechT5Model""",
     """SpeechT5PreTrainedModel""",
     """SpeechT5HifiGan""",
 ]

if TYPE_CHECKING:
 from .configuration_speechta import (
     SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
     SpeechTaConfig,
     SpeechTaHifiGanConfig,
 )
 from .feature_extraction_speechta import SpeechTaFeatureExtractor
 from .processing_speechta import SpeechTaProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speechta import SpeechTaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speechta import (
      SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechTaForSpeechToSpeech,
      SpeechTaForSpeechToText,
      SpeechTaForTextToSpeech,
      SpeechTaHifiGan,
      SpeechTaModel,
      SpeechTaPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE		:List[str]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
