

from typing import TYPE_CHECKING

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


lowerCAmelCase__		      =       {
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase__		      =       [
	    """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """LongformerForMaskedLM""",
	    """LongformerForMultipleChoice""",
	    """LongformerForQuestionAnswering""",
	    """LongformerForSequenceClassification""",
	    """LongformerForTokenClassification""",
	    """LongformerModel""",
	    """LongformerPreTrainedModel""",
	    """LongformerSelfAttention""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase__		      =       [
	    """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFLongformerForMaskedLM""",
	    """TFLongformerForMultipleChoice""",
	    """TFLongformerForQuestionAnswering""",
	    """TFLongformerForSequenceClassification""",
	    """TFLongformerForTokenClassification""",
	    """TFLongformerModel""",
	    """TFLongformerPreTrainedModel""",
	    """TFLongformerSelfAttention""",
	]


if TYPE_CHECKING:
	from .configuration_longformer import (
	    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    LongformerConfig,
	    LongformerOnnxConfig,
	)
	from .tokenization_longformer import LongformerTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_longformer_fast import LongformerTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_longformer import (
		    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LongformerForMaskedLM,
		    LongformerForMultipleChoice,
		    LongformerForQuestionAnswering,
		    LongformerForSequenceClassification,
		    LongformerForTokenClassification,
		    LongformerModel,
		    LongformerPreTrainedModel,
		    LongformerSelfAttention,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_longformer import (
		    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLongformerForMaskedLM,
		    TFLongformerForMultipleChoice,
		    TFLongformerForQuestionAnswering,
		    TFLongformerForSequenceClassification,
		    TFLongformerForTokenClassification,
		    TFLongformerModel,
		    TFLongformerPreTrainedModel,
		    TFLongformerSelfAttention,
		)

else:
	import sys

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





def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 4000000	):


  '''simple docstring'''

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

  return total


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



