


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_snake_case          =							{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							["""PerceiverFeatureExtractor"""]
   _snake_case          =							["""PerceiverImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """PerceiverForImageClassificationConvProcessing""",
       """PerceiverForImageClassificationFourier""",
       """PerceiverForImageClassificationLearned""",
       """PerceiverForMaskedLM""",
       """PerceiverForMultimodalAutoencoding""",
       """PerceiverForOpticalFlow""",
       """PerceiverForSequenceClassification""",
       """PerceiverLayer""",
       """PerceiverModel""",
       """PerceiverPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
   from .tokenization_perceiver import PerceiverTokenizer

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_perceiver import PerceiverFeatureExtractor
      from .image_processing_perceiver import PerceiverImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_perceiver import (
          PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
          PerceiverForImageClassificationConvProcessing,
          PerceiverForImageClassificationFourier,
          PerceiverForImageClassificationLearned,
          PerceiverForMaskedLM,
          PerceiverForMultimodalAutoencoding,
          PerceiverForOpticalFlow,
          PerceiverForSequenceClassification,
          PerceiverLayer,
          PerceiverModel,
          PerceiverPreTrainedModel,
      )

else:
   import sys

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







"""simple docstring"""


def   snake_case						(				_a:     int = 4000000							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		[0, 1]
      lowerCamelCase__           =		0
      while fib[i] <= n:
            fib.append(fib[i] + fib[i + 1]							)
            if fib[i + 2] > n:
                  break
            i += 1
      lowerCamelCase__           =		0
      for j in range(len(_a							) - 1							):
            if fib[j] % 2 == 0:
                  total += fib[j]

      return total


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



