





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,
)


snake_case 				=       {
    """configuration_xlm_roberta""": [
        """XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaConfig""",
        """XLMRobertaOnnxConfig""",
    ],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""XLMRobertaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""XLMRobertaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """XLMRobertaForCausalLM""",
          """XLMRobertaForMaskedLM""",
          """XLMRobertaForMultipleChoice""",
          """XLMRobertaForQuestionAnswering""",
          """XLMRobertaForSequenceClassification""",
          """XLMRobertaForTokenClassification""",
          """XLMRobertaModel""",
          """XLMRobertaPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFXLMRobertaForCausalLM""",
          """TFXLMRobertaForMaskedLM""",
          """TFXLMRobertaForMultipleChoice""",
          """TFXLMRobertaForQuestionAnswering""",
          """TFXLMRobertaForSequenceClassification""",
          """TFXLMRobertaForTokenClassification""",
          """TFXLMRobertaModel""",
          """TFXLMRobertaPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """FlaxXLMRobertaForMaskedLM""",
          """FlaxXLMRobertaForCausalLM""",
          """FlaxXLMRobertaForMultipleChoice""",
          """FlaxXLMRobertaForQuestionAnswering""",
          """FlaxXLMRobertaForSequenceClassification""",
          """FlaxXLMRobertaForTokenClassification""",
          """FlaxXLMRobertaModel""",
          """FlaxXLMRobertaPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_xlm_roberta import (
          XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
          XLMRobertaConfig,
          XLMRobertaOnnxConfig,
      )

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_roberta import XLMRobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_roberta import (
                XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMRobertaForCausalLM,
                XLMRobertaForMaskedLM,
                XLMRobertaForMultipleChoice,
                XLMRobertaForQuestionAnswering,
                XLMRobertaForSequenceClassification,
                XLMRobertaForTokenClassification,
                XLMRobertaModel,
                XLMRobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_xlm_roberta import (
                TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFXLMRobertaForCausalLM,
                TFXLMRobertaForMaskedLM,
                TFXLMRobertaForMultipleChoice,
                TFXLMRobertaForQuestionAnswering,
                TFXLMRobertaForSequenceClassification,
                TFXLMRobertaForTokenClassification,
                TFXLMRobertaModel,
                TFXLMRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_xlm_roberta import (
                FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxXLMRobertaForCausalLM,
                FlaxXLMRobertaForMaskedLM,
                FlaxXLMRobertaForMultipleChoice,
                FlaxXLMRobertaForQuestionAnswering,
                FlaxXLMRobertaForSequenceClassification,
                FlaxXLMRobertaForTokenClassification,
                FlaxXLMRobertaModel,
                FlaxXLMRobertaPreTrainedModel,
            )

else:
      import sys

      snake_case 				=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






from __future__ import annotations

from collections.abc import Generator

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

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :float = 1E10     )      ->						int:
  _lowercase							= sieve()

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


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