from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE     =     {
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE     =     ["""GPT2TokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE     =     [
         """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GPT2DoubleHeadsModel""",
         """GPT2ForQuestionAnswering""",
         """GPT2ForSequenceClassification""",
         """GPT2ForTokenClassification""",
         """GPT2LMHeadModel""",
         """GPT2Model""",
         """GPT2PreTrainedModel""",
         """load_tf_weights_in_gpt2""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE     =     [
         """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFGPT2DoubleHeadsModel""",
         """TFGPT2ForSequenceClassification""",
         """TFGPT2LMHeadModel""",
         """TFGPT2MainLayer""",
         """TFGPT2Model""",
         """TFGPT2PreTrainedModel""",
     ]

try:
     if not is_keras_nlp_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE     =     ["""TFGPT2Tokenizer"""]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE     =     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
     from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
     from .tokenization_gpta import GPTaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_gpta_fast import GPTaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpta import (
              GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTaDoubleHeadsModel,
              GPTaForQuestionAnswering,
              GPTaForSequenceClassification,
              GPTaForTokenClassification,
              GPTaLMHeadModel,
              GPTaModel,
              GPTaPreTrainedModel,
              load_tf_weights_in_gpta,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gpta import (
              TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGPTaDoubleHeadsModel,
              TFGPTaForSequenceClassification,
              TFGPTaLMHeadModel,
              TFGPTaMainLayer,
              TFGPTaModel,
              TFGPTaPreTrainedModel,
          )

     try:
          if not is_keras_nlp_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_gpta_tf import TFGPTaTokenizer

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
     import sys

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


import math
def 					__lowercase						(    lowerCamelCase__       )					->bool:

  '''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(lowerCamelCase__       ) + 1       )			,					6       ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True
def 					__lowercase						(    lowerCamelCase__ = 2_00_00_00       )					->int:

  '''simple docstring'''




  return sum(num for num in range(3			,					lowerCamelCase__			,					2       ) if is_prime(lowerCamelCase__       )       ) + 2 if n > 2 else 0


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

