'''simple docstring'''

import math


def __a (	__lowerCamelCase			:						int				)	->							list[int]:
      '''simple docstring'''


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

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

      lowercase_     =      end + 1
      lowercase_     =      min(2 * end ,		__lowerCamelCase				)

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

                  for j in range(__lowerCamelCase ,		high + 1 ,		__lowerCamelCase				):
                        lowercase_     =      False

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

            lowercase_     =      high + 1
            lowercase_     =      min(high + end ,		__lowerCamelCase				)

      return prime






print(sieve(10**6))







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_ : str						  =      {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Union[str, Any]						  =      [
           "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Wav2Vec2ForAudioFrameClassification",
           "Wav2Vec2ForCTC",
           "Wav2Vec2ForMaskedLM",
           "Wav2Vec2ForPreTraining",
           "Wav2Vec2ForSequenceClassification",
           "Wav2Vec2ForXVector",
           "Wav2Vec2Model",
           "Wav2Vec2PreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : int						  =      [
           "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFWav2Vec2ForCTC",
           "TFWav2Vec2Model",
           "TFWav2Vec2PreTrainedModel",
           "TFWav2Vec2ForSequenceClassification",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Dict						  =      [
           "FlaxWav2Vec2ForCTC",
           "FlaxWav2Vec2ForPreTraining",
           "FlaxWav2Vec2Model",
           "FlaxWav2Vec2PreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
       from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
       from .processing_wavaveca import WavaVecaProcessor
       from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavaveca import (
                  WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavaVecaForAudioFrameClassification,
                  WavaVecaForCTC,
                  WavaVecaForMaskedLM,
                  WavaVecaForPreTraining,
                  WavaVecaForSequenceClassification,
                  WavaVecaForXVector,
                  WavaVecaModel,
                  WavaVecaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFWavaVecaForCTC,
                  TFWavaVecaForSequenceClassification,
                  TFWavaVecaModel,
                  TFWavaVecaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  FlaxWavaVecaForCTC,
                  FlaxWavaVecaForPreTraining,
                  FlaxWavaVecaModel,
                  FlaxWavaVecaPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_ : Optional[int]						  =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






