
"""simple docstring"""


import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )       ->				Any:
	_lowerCAmelCase			=ConvBertConfig.from_json_file(__UpperCamelCase )
	_lowerCAmelCase			=ConvBertModel(__UpperCamelCase )

	_lowerCAmelCase			=load_tf_weights_in_convbert(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )
	model.save_pretrained(__UpperCamelCase )

	_lowerCAmelCase			=TFConvBertModel.from_pretrained(__UpperCamelCase					,				from_pt=__UpperCamelCase )
	tf_model.save_pretrained(__UpperCamelCase )


if __name__ == "__main__":
			__A 		= argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
			)
			parser.add_argument(
			    '--convbert_config_file',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        'The config json file corresponding to the pre-trained ConvBERT model. \n'
			        'This specifies the model architecture.'
			    ),
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			__A 		= parser.parse_args()
			convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)





"""simple docstring"""


# 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,
)


__A 		= {
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= [
			    '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

			__A 		= _LazyModule(__name__, globals()['__file__'], _import_structure)



