
'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()



def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Union[str, Any]	):
						# Initialise PyTorch model
						_SCREAMING_SNAKE_CASE					=			RoFormerConfig.from_json_file(_SCREAMING_SNAKE_CASE	)
						print(F'Building PyTorch model from configuration: {config}'	)
						_SCREAMING_SNAKE_CASE					=			RoFormerForMaskedLM(_SCREAMING_SNAKE_CASE	)

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

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


if __name__ == "__main__":
						_snake_case					: Dict     			=					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."""
						)
						_snake_case					: int     			=					parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

'''simple docstring'''







from ..utils import DummyObject, requires_backends


class       lowerCAmelCase			(					metaclass=__UpperCAmelCase      ):
		a				:       Any							=       ["""torch""", """transformers""", """invisible_watermark"""]




		def __init__(					self       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(self       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )


class       lowerCAmelCase			(					metaclass=__UpperCAmelCase      ):
		a				:       Optional[Any]							=       ["""torch""", """transformers""", """invisible_watermark"""]




		def __init__(					self       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(self       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )