



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE     				=						{
    "configuration_efficientformer": [
        "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientFormerConfig",
    ]
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["EfficientFormerImageProcessor"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "EfficientFormerForImageClassification",
			    "EfficientFormerForImageClassificationWithTeacher",
			    "EfficientFormerModel",
			    "EfficientFormerPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFEfficientFormerForImageClassification",
			    "TFEfficientFormerForImageClassificationWithTeacher",
			    "TFEfficientFormerModel",
			    "TFEfficientFormerPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_efficientformer import EfficientFormerImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_efficientformer import (
						    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    EfficientFormerForImageClassification,
						    EfficientFormerForImageClassificationWithTeacher,
						    EfficientFormerModel,
						    EfficientFormerPreTrainedModel,
						)
			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_efficientformer import (
						    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFEfficientFormerForImageClassification,
						    TFEfficientFormerForImageClassificationWithTeacher,
						    TFEfficientFormerModel,
						    TFEfficientFormerPreTrainedModel,
						)

else:
			import sys

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






def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							int:
		UpperCAmelCase_						=				[1]

		UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_						=				0, 0, 0
		UpperCAmelCase_						=				ugly_nums[ia] * 2
		UpperCAmelCase_						=				ugly_nums[ia] * 3
		UpperCAmelCase_						=				ugly_nums[ia] * 5

		for _ in range(1			,     __SCREAMING_SNAKE_CASE ):
				UpperCAmelCase_						=				min(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
				ugly_nums.append(__SCREAMING_SNAKE_CASE )
				if next_num == next_a:
						ia += 1
						UpperCAmelCase_						=				ugly_nums[ia] * 2
				if next_num == next_a:
						ia += 1
						UpperCAmelCase_						=				ugly_nums[ia] * 3
				if next_num == next_a:
						ia += 1
						UpperCAmelCase_						=				ugly_nums[ia] * 5
		return ugly_nums[-1]


if __name__ == "__main__":
			from doctest import testmod

			testmod(verbose=True)
			print(f'''{ugly_numbers(200) = }''')

