



'''simple docstring'''

import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[Any]:
		# Initialise PyTorch model
		_a     :      Optional[int]        =							AlbertConfig.from_json_file(lowerCAmelCase_  )
		print(f"""Building PyTorch model from configuration: {config}"""  )
		_a     :      Any        =							AlbertForPreTraining(lowerCAmelCase_  )

		# Load weights from tf checkpoint
		load_tf_weights_in_albert(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )

		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""  )
		torch.save(model.state_dict()						,			lowerCAmelCase_  )


if __name__ == "__main__":
							__lowerCAmelCase									=   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(
							    '''--albert_config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        '''The config json file corresponding to the pre-trained ALBERT 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.'''
							)
							__lowerCAmelCase									=   parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BigBirdPegasusForCausalLM''',
							    '''BigBirdPegasusForConditionalGeneration''',
							    '''BigBirdPegasusForQuestionAnswering''',
							    '''BigBirdPegasusForSequenceClassification''',
							    '''BigBirdPegasusModel''',
							    '''BigBirdPegasusPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_bigbird_pegasus import (
							    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BigBirdPegasusConfig,
							    BigBirdPegasusOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bigbird_pegasus import (
														    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BigBirdPegasusForCausalLM,
														    BigBirdPegasusForConditionalGeneration,
														    BigBirdPegasusForQuestionAnswering,
														    BigBirdPegasusForSequenceClassification,
														    BigBirdPegasusModel,
														    BigBirdPegasusPreTrainedModel,
														)


else:
							import sys

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




