



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def _lowercase   (  __A							,__A							,__A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
 print(f"Building PyTorch model from configuration: {config}"       )
 __UpperCamelCase				      =  TaForConditionalGeneration(__A       )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(__A							,__A							,__A       )

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"       )
 model.save_pretrained(__A       )


if __name__ == "__main__":
 a__						:			int										=			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(
     '--config_file',
     default=None,
     type=str,
     required=True,
     help=(
         'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 a__						:			Optional[Any]										=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a__						:			Optional[int]										=			{
    'configuration_bark': [
        'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BarkCoarseConfig',
        'BarkConfig',
        'BarkFineConfig',
        'BarkSemanticConfig',
    ],
    'processing_bark': ['BarkProcessor'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__						:			Any										=			[
     'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BarkFineModel',
     'BarkSemanticModel',
     'BarkCoarseModel',
     'BarkModel',
     'BarkPreTrainedModel',
     'BarkCausalModel',
 ]

if TYPE_CHECKING:
 from .configuration_bark import (
     BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BarkCoarseConfig,
     BarkConfig,
     BarkFineConfig,
     BarkSemanticConfig,
 )
 from .processing_bark import BarkProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bark import (
      BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
      BarkCausalModel,
      BarkCoarseModel,
      BarkFineModel,
      BarkModel,
      BarkPreTrainedModel,
      BarkSemanticModel,
  )

else:
 import sys

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





