





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_		:				Any				=						{
    '''configuration_clap''': [
        '''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ClapAudioConfig''',
        '''ClapConfig''',
        '''ClapTextConfig''',
    ],
    '''processing_clap''': ['''ClapProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				int				=						[
		    '''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ClapModel''',
		    '''ClapPreTrainedModel''',
		    '''ClapTextModel''',
		    '''ClapTextModelWithProjection''',
		    '''ClapAudioModel''',
		    '''ClapAudioModelWithProjection''',
		]
		lowercase_		:				int				=						['''ClapFeatureExtractor''']

if TYPE_CHECKING:
		from .configuration_clap import (
		    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ClapAudioConfig,
		    ClapConfig,
		    ClapTextConfig,
		)
		from .processing_clap import ClapProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clap import ClapFeatureExtractor
				from .modeling_clap import (
				    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ClapAudioModel,
				    ClapAudioModelWithProjection,
				    ClapModel,
				    ClapPreTrainedModel,
				    ClapTextModel,
				    ClapTextModelWithProjection,
				)


else:
		import sys

		lowercase_		:				Tuple				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)













import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				# Initialise PyTorch model
				_snake_case :			Optional[int]  						=   BertConfig.from_json_file(__lowerCAmelCase )
				print(F'''Building PyTorch model from configuration: {config}''' )
				_snake_case :			List[str]  						=   BertForPreTraining(__lowerCAmelCase )

				# Load weights from tf checkpoint
				load_tf_weights_in_bert(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}''' )
				torch.save(model.state_dict()	,			__lowerCAmelCase )


if __name__ == "__main__":
		lowercase_		:				Any				=						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(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT 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.'''
		)
		lowercase_		:				List[str]				=						parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






