


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__snake_case     =		logging.get_logger(__name__)

__snake_case     =		['''UtteranceLevel''']






@torch.no_grad()
def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				str:


     """simple docstring"""


     __UpperCamelCase				=	torch.load(_lowercase			,       map_location='cpu'				)
     if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
          raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''				)

     __UpperCamelCase				=	checkpoint['Downstream']

     __UpperCamelCase				=	HubertConfig.from_pretrained(_lowercase				)
     __UpperCamelCase				=	HubertForSequenceClassification.from_pretrained(_lowercase			,       config=_lowercase				)
     __UpperCamelCase				=	WavaVecaFeatureExtractor.from_pretrained(
         _lowercase			,       return_attention_mask=_lowercase			,       do_normalize=_lowercase				)

     if hf_congfig.use_weighted_layer_sum:
          __UpperCamelCase				=	checkpoint['Featurizer']['weights']

     __UpperCamelCase				=	downstream_dict['projector.weight']
     __UpperCamelCase				=	downstream_dict['projector.bias']
     __UpperCamelCase				=	downstream_dict['model.post_net.linear.weight']
     __UpperCamelCase				=	downstream_dict['model.post_net.linear.bias']

     hf_feature_extractor.save_pretrained(_lowercase				)
     hf_model.save_pretrained(_lowercase				)


if __name__ == "__main__":
    __snake_case     =		argparse.ArgumentParser()
    parser.add_argument(
        '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
    )
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
    parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
    parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
    __snake_case     =		parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






# 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,
)


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

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

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


