

'''simple docstring'''






from typing import TYPE_CHECKING

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


lowerCAmelCase__      						=			{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__      						=			[
     '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''UniSpeechForCTC''',
     '''UniSpeechForPreTraining''',
     '''UniSpeechForSequenceClassification''',
     '''UniSpeechModel''',
     '''UniSpeechPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_unispeech import (
      UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
      UniSpeechForCTC,
      UniSpeechForPreTraining,
      UniSpeechForSequenceClassification,
      UniSpeechModel,
      UniSpeechPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase__      						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




'''simple docstring'''






import argparse

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


logging.set_verbosity_info()


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				TaConfig.from_json_file(A__   )
 print(F"Building PyTorch model from configuration: {config}"   )
 __lowercase													=				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__":
 lowerCAmelCase__      		=			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.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

