


from typing import TYPE_CHECKING

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


lowerCAmelCase           =		{'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		[
	    'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'OPTForCausalLM',
	    'OPTModel',
	    'OPTPreTrainedModel',
	    'OPTForSequenceClassification',
	    'OPTForQuestionAnswering',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		[
	    'FlaxOPTForCausalLM',
	    'FlaxOPTModel',
	    'FlaxOPTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

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














"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int = 4_00_00_00    ):





  '''simple docstring'''





  lowercase							    =       []
  lowercase    ,     lowercase							    =       0, 1
  while b <= n:
    if b % 2 == 0:
      even_fibs.append(__snake_case    )
    lowercase    ,     lowercase							    =       b, a + b
  return sum(__snake_case    )


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


