
import argparse

import torch

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


logging.set_verbosity_info()


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  Optional[int]:
				# Initialise PyTorch model
				__a										=    RemBertConfig.from_json_file(a__  )
				print('''Building PyTorch model from configuration: {}'''.format(str(a__  )  )  )
				__a										=    RemBertModel(a__  )

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

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


if __name__ == "__main__":
					A     :	str    = 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     :	Tuple    = 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__							:							List[Any] ={
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__							:							Optional[int] =[
        '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TimesformerModel''',
        '''TimesformerForVideoClassification''',
        '''TimesformerPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timesformer import (
            TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TimesformerForVideoClassification,
            TimesformerModel,
            TimesformerPreTrainedModel,
        )

else:
    import sys

    a__							:							Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
