






'''simple docstring'''







import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Union[str, Any]:
							# Initialise PyTorch model
							lowerCamelCase__					:	Optional[Any]         =       RemBertConfig.from_json_file(UpperCamelCase      )
							print("""Building PyTorch model from configuration: {}""".format(str(UpperCamelCase      )      )      )
							lowerCamelCase__					:	Optional[Any]         =       RemBertModel(UpperCamelCase      )

							# Load weights from tf checkpoint
							load_tf_weights_in_rembert(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )

							# Save pytorch-model
							print("""Save PyTorch model to {}""".format(UpperCamelCase      )      )
							torch.save(model.state_dict()				,			UpperCamelCase      )


if __name__ == "__main__":
			_A	:			Union[str, 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(
			    '''--rembert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained RemBERT 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.'''
			)
			_A	:			Union[str, Any]         =parser.parse_args()
			convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)









'''simple docstring'''







from typing import TYPE_CHECKING

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


_A	:			Union[str, Any]         ={
    '''configuration_swiftformer''': [
        '''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwiftFormerConfig''',
        '''SwiftFormerOnnxConfig''',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			Optional[Any]         =[
			    '''SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SwiftFormerForImageClassification''',
			    '''SwiftFormerModel''',
			    '''SwiftFormerPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_swiftformer import (
			    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SwiftFormerConfig,
			    SwiftFormerOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swiftformer import (
						    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwiftFormerForImageClassification,
						    SwiftFormerModel,
						    SwiftFormerPreTrainedModel,
						)

else:
			import sys

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

