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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async





def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]=None    )			-> Union[str, Any]:





	"""simple docstring"""







	if subparsers is not None:
		A             =					subparsers.add_parser("""test"""    )
	else:
		A             =					argparse.ArgumentParser("""Accelerate test command"""    )

	parser.add_argument(
	    """--config_file"""				,       default=UpperCamelCase__				,       help=(
	        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
	        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
	        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
	        """with 'huggingface'."""
	    )				,       )

	if subparsers is not None:
		parser.set_defaults(func=UpperCamelCase__    )
	return parser





def 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]    )			-> Any:





	"""simple docstring"""







	A             =					os.path.sep.join(__file__.split(os.path.sep    )[:-2] + ["""test_utils""", """scripts""", """test_script.py"""]    )

	if args.config_file is None:
		A             =					script_name
	else:
		A             =					f'--config_file={args.config_file} {script_name}'

	A             =					["""accelerate-launch"""] + test_args.split()
	A             =					execute_subprocess_async(UpperCamelCase__				,       env=os.environ.copy()    )
	if result.returncode == 0:
		print("""Test is a success! You are ready for your distributed training!"""    )





def 	_lowerCAmelCase					( )			-> List[Any]:





	"""simple docstring"""







	A             =					test_command_parser()
	A             =					parser.parse_args()
	test_command(UpperCamelCase__    )


if __name__ == "__main__":
				main()







from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:   Any						=						logging.get_logger(__name__)

_lowercase							:   Union[str, Any]						=						{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			'efficientformer'

				def __init__(    self					, a__ = [3, 2, 6, 4]					, a__ = [48, 96, 224, 448]					, a__ = [True, True, True, True]					, a__ = 448					, a__ = 32					, a__ = 4					, a__ = 7					, a__ = 5					, a__ = 8					, a__ = 4					, a__ = 0.0					, a__ = 16					, a__ = 3					, a__ = 3					, a__ = 3					, a__ = 2					, a__ = 1					, a__ = 0.0					, a__ = 1					, a__ = True					, a__ = True					, a__ = 1e-5					, a__ = "gelu"					, a__ = 0.02					, a__ = 1e-12					, a__ = 224					, a__ = 1e-05					, **a__					, )						->					None:
					super().__init__(**a__ )

					A             =					hidden_act
					A             =					hidden_dropout_prob
					A             =					hidden_sizes
					A             =					num_hidden_layers
					A             =					num_attention_heads
					A             =					initializer_range
					A             =					layer_norm_eps
					A             =					patch_size
					A             =					num_channels
					A             =					depths
					A             =					mlp_expansion_ratio
					A             =					downsamples
					A             =					dim
					A             =					key_dim
					A             =					attention_ratio
					A             =					resolution
					A             =					pool_size
					A             =					downsample_patch_size
					A             =					downsample_stride
					A             =					downsample_pad
					A             =					drop_path_rate
					A             =					num_metaad_blocks
					A             =					distillation
					A             =					use_layer_scale
					A             =					layer_scale_init_value
					A             =					image_size
					A             =					batch_norm_eps



