



import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()





def     __lowerCamelCase							(   __magic_name__						:	Union[str, Any]  ,			__magic_name__						:	Tuple    ):
							# Initialize PyTorch model
							a__:							Union[str, Any]									=CanineConfig()
							a__:							Optional[int]									=CanineModel(__magic_name__    )
							model.eval()

							print(F"Building PyTorch model from configuration: {config}"    )

							# Load weights from tf checkpoint
							load_tf_weights_in_canine(__magic_name__  ,			__magic_name__  ,			__magic_name__    )

							# Save pytorch-model (weights and configuration)
							print(F"Save PyTorch model to {pytorch_dump_path}"    )
							model.save_pretrained(__magic_name__    )

							# Save tokenizer files
							a__:							str									=CanineTokenizer()
							print(F"Save tokenizer files to {pytorch_dump_path}"    )
							tokenizer.save_pretrained(__magic_name__    )


if __name__ == "__main__":
		__UpperCAmelCase    	=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
		)
		parser.add_argument(
		    '''--pytorch_dump_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to a folder where the PyTorch model will be placed.''',
		)
		__UpperCAmelCase    	=  parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)










from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__UpperCAmelCase    	=  {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    	=  ['''ErnieMTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    	=  [
		    '''ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ErnieMForMultipleChoice''',
		    '''ErnieMForQuestionAnswering''',
		    '''ErnieMForSequenceClassification''',
		    '''ErnieMForTokenClassification''',
		    '''ErnieMModel''',
		    '''ErnieMPreTrainedModel''',
		    '''ErnieMForInformationExtraction''',
		]


if TYPE_CHECKING:
		from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_ernie_m import ErnieMTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ernie_m import (
				    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ErnieMForInformationExtraction,
				    ErnieMForMultipleChoice,
				    ErnieMForQuestionAnswering,
				    ErnieMForSequenceClassification,
				    ErnieMForTokenClassification,
				    ErnieMModel,
				    ErnieMPreTrainedModel,
				)


else:
		import sys

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





