




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__            =     {
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     [
					    """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ConvNextV2ForImageClassification""",
					    """ConvNextV2Model""",
					    """ConvNextV2PreTrainedModel""",
					    """ConvNextV2Backbone""",
					]


if TYPE_CHECKING:
					from .configuration_convnextva import (
					    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ConvNextVaConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_convnextva import (
										    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ConvNextVaBackbone,
										    ConvNextVaForImageClassification,
										    ConvNextVaModel,
										    ConvNextVaPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase__            =     _LazyModule(__name__, globals()["""__file__"""], _import_structure)










import json
import logging
import os
import subprocess
from argparse import ArgumentParser


UpperCamelCase__            =     logging.getLogger(__name__)







def       _a (		):
					__lowerCAmelCase							  =			ArgumentParser()
					__lowerCAmelCase  ,				__lowerCAmelCase							  =			parser.parse_known_args()
					for arg in unknown:
										if arg.startswith(("-", "--")	):
															parser.add_argument(arg.split("="	)[0]	)

					return parser.parse_args()







def       _a (		):
					__lowerCAmelCase							  =			parse_args()
					__lowerCAmelCase							  =			88_88
					__lowerCAmelCase							  =			int(os.environ["SM_NUM_GPUS"]	)
					__lowerCAmelCase							  =			json.loads(os.environ["SM_HOSTS"]	)
					__lowerCAmelCase							  =			len(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			os.environ["SM_CURRENT_HOST"]
					__lowerCAmelCase							  =			hosts.index(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			"INFO"

					if num_nodes > 1:
										__lowerCAmelCase							  =			F"""python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {''.join(['nested fstring' for parameter,value in args.__dict__.items()]	)}"""
					else:
										__lowerCAmelCase							  =			F"""python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {''.join(['nested fstring' for parameter,value in args.__dict__.items()]	)}"""
					try:
										subprocess.run(SCREAMING_SNAKE_CASE_ , shell=SCREAMING_SNAKE_CASE_	)
					except Exception as e:
										logger.info(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
					main()




