





'''simple docstring'''

from __future__ import annotations





def 		UpperCamelCase							(				_lowerCamelCase   :   int			):
    A__							=     [True] * limit
    A__							=     False
    A__							=     False
    A__							=     True

    for i in range(3		,			int(limit**0.5 + 1			)		,			2			):
        A__							=     i * 2
        while index < limit:
            A__							=     False
            A__							=     index + i

    A__							=     [2]

    for i in range(3		,			_lowerCamelCase		,			2			):
        if is_prime[i]:
            primes.append(_lowerCamelCase			)

    return primes





def 		UpperCamelCase							(				_lowerCamelCase   :   int = 1_00_00_00			):
    A__							=     prime_sieve(_lowerCamelCase			)
    A__							=     0
    A__							=     0

    for i in range(len(_lowerCamelCase			)			):
        for j in range(i + length		,			len(_lowerCamelCase			)			):
            A__							=     sum(primes[i:j]			)
            if sol >= ceiling:
                break

            if sol in primes:
                A__							=     j - i
                A__							=     sol

    return largest


if __name__ == "__main__":
  print(f"""{solution() = }""")










'''simple docstring'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase				:      Union[str, Any]						      ={"configuration_mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =[
      "MRA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MraForMaskedLM",
      "MraForMultipleChoice",
      "MraForQuestionAnswering",
      "MraForSequenceClassification",
      "MraForTokenClassification",
      "MraLayer",
      "MraModel",
      "MraPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mra import (
        MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraLayer,
        MraModel,
        MraPreTrainedModel,
    )
else:
  import sys

  __lowerCAmelCase				:      Dict						      =_LazyModule(__name__, globals()["__file__"], _import_structure)



