




"""simple docstring"""


from typing import TYPE_CHECKING

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


A_      			=       {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      			=       ['''RoFormerTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      			=       [
         '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RoFormerForCausalLM''',
         '''RoFormerForMaskedLM''',
         '''RoFormerForMultipleChoice''',
         '''RoFormerForQuestionAnswering''',
         '''RoFormerForSequenceClassification''',
         '''RoFormerForTokenClassification''',
         '''RoFormerLayer''',
         '''RoFormerModel''',
         '''RoFormerPreTrainedModel''',
         '''load_tf_weights_in_roformer''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      			=       [
         '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFRoFormerForCausalLM''',
         '''TFRoFormerForMaskedLM''',
         '''TFRoFormerForMultipleChoice''',
         '''TFRoFormerForQuestionAnswering''',
         '''TFRoFormerForSequenceClassification''',
         '''TFRoFormerForTokenClassification''',
         '''TFRoFormerLayer''',
         '''TFRoFormerModel''',
         '''TFRoFormerPreTrainedModel''',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      			=       [
         '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''FlaxRoFormerForMaskedLM''',
         '''FlaxRoFormerForMultipleChoice''',
         '''FlaxRoFormerForQuestionAnswering''',
         '''FlaxRoFormerForSequenceClassification''',
         '''FlaxRoFormerForTokenClassification''',
         '''FlaxRoFormerModel''',
         '''FlaxRoFormerPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
     from .tokenization_roformer import RoFormerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_roformer_fast import RoFormerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roformer import (
              ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              RoFormerForCausalLM,
              RoFormerForMaskedLM,
              RoFormerForMultipleChoice,
              RoFormerForQuestionAnswering,
              RoFormerForSequenceClassification,
              RoFormerForTokenClassification,
              RoFormerLayer,
              RoFormerModel,
              RoFormerPreTrainedModel,
              load_tf_weights_in_roformer,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roformer import (
              TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForMultipleChoice,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerLayer,
              TFRoFormerModel,
              TFRoFormerPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roformer import (
              FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              FlaxRoFormerForMaskedLM,
              FlaxRoFormerForMultipleChoice,
              FlaxRoFormerForQuestionAnswering,
              FlaxRoFormerForSequenceClassification,
              FlaxRoFormerForTokenClassification,
              FlaxRoFormerModel,
              FlaxRoFormerPreTrainedModel,
          )


else:
     import sys

     A_      			=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












"""simple docstring"""


from __future__ import annotations

import math


def       UpperCAmelCase__ (snake_case__							:      int					):
  """simple docstring"""

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5	,     int(math.sqrt(snake_case__					) + 1					)	,     6					):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True


A_      			=       [num for num in range(3, 10_00_01, 2) if not is_prime(num)]


def       UpperCAmelCase__ (snake_case__							:      int					):
  """simple docstring"""

  if not isinstance(snake_case__	,     snake_case__					):
    raise ValueError("""n must be an integer"""					)
  if n <= 0:
    raise ValueError("""n must be >= 0"""					)

  _snake_case							:	Any			=						[]
  for num in range(len(snake_case__					)					):
    _snake_case							:	Optional[int]			=						0
    while 2 * i * i <= odd_composites[num]:
      _snake_case							:	Optional[int]			=						odd_composites[num] - 2 * i * i
      if is_prime(snake_case__					):
        break
      i += 1
    else:
      list_nums.append(odd_composites[num]					)
      if len(snake_case__					) == n:
        return list_nums

  return []


def       UpperCAmelCase__ ():
  """simple docstring"""

  return compute_nums(1					)[0]


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


