

from pathlib import Path

import fire

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							Path(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							Path(_a)
	dest_dir.mkdir(exist_ok=_a)
	for path in src_dir.iterdir():
		SCREAMING_SNAKE_CASE :   List[Any]					  =							[x.rstrip() for x in list(path.open().readlines())][:n]
		SCREAMING_SNAKE_CASE :   str					  =							dest_dir.joinpath(path.name)
		print(_a)
		dest_path.open("w").write("\n".join(_a))


if __name__ == "__main__":
			fire.Fire(minify)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_      =     {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'Swinv2ForImageClassification',
			    'Swinv2ForMaskedImageModeling',
			    'Swinv2Model',
			    'Swinv2PreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

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