

from typing import TYPE_CHECKING

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


A__						=							{
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						=							[
						    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BlenderbotSmallForCausalLM",
						    "BlenderbotSmallForConditionalGeneration",
						    "BlenderbotSmallModel",
						    "BlenderbotSmallPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						=							[
						    "TFBlenderbotSmallForConditionalGeneration",
						    "TFBlenderbotSmallModel",
						    "TFBlenderbotSmallPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						=							[
						    "FlaxBlenderbotSmallForConditionalGeneration",
						    "FlaxBlenderbotSmallModel",
						    "FlaxBlenderbotSmallPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_blenderbot_small import (
						    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    BlenderbotSmallConfig,
						    BlenderbotSmallOnnxConfig,
						)
						from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_blenderbot_small import (
												    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BlenderbotSmallForCausalLM,
												    BlenderbotSmallForConditionalGeneration,
												    BlenderbotSmallModel,
												    BlenderbotSmallPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_blenderbot_small import (
												    TFBlenderbotSmallForConditionalGeneration,
												    TFBlenderbotSmallModel,
												    TFBlenderbotSmallPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_blenderbot_small import (
												    FlaxBlenderbotSmallForConditionalGeneration,
												    FlaxBlenderbotSmallModel,
												    FlaxBlenderbotSmallPreTrainedModel,
												)

else:
						import sys

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



from __future__ import annotations




def 					_lowercase     (				a_				:					list[int]				)				->	int:




					'''simple docstring'''







					if not nums:
										return 0
					__magic_name__							=    nums[0]
					__magic_name__							=    0
					for num in nums[1:]:
										__magic_name__,	__magic_name__							=    (
										    max_excluding + num,
										    max(a_					,a_				),
										)
					return max(a_					,a_				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




