
'''simple docstring'''


import argparse

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


logging.set_verbosity_info()




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      Union[str, Any]     =		TaConfig.from_json_file(_lowercase				)
					print(F"""Building PyTorch model from configuration: {config}"""				)
					lowerCamelCase_					:      List[Any]     =		TaForConditionalGeneration(_lowercase				)

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

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


if __name__ == "__main__":
					__lowercase							:   Optional[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(
					    '''--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.'''
					)
					__lowercase							:   Tuple					=     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_sentencepiece_available


__lowercase							:   List[Any]					=     {}


try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:   List[Any]					=     ['''MLukeTokenizer''']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mluke import MLukeTokenizer


else:
					import sys

					__lowercase							:   List[str]					=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


