





"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()

def       __A (     a_  :Optional[Any]				,    a_  :Optional[int]				,    a_  :Any)						->						Union[str, Any]:
				# Initialise PyTorch model
				__a	:  Dict         =					AlbertConfig.from_json_file(a_)
				print(F"""Building PyTorch model from configuration: {config}""")
				__a	:  List[Any]         =					AlbertForPreTraining(a_)

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

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


if __name__ == "__main__":
		A	     =					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.'''
		)
		A	     =					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


A	     =					{
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					[
		    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''InstructBlipQFormerModel''',
		    '''InstructBlipPreTrainedModel''',
		    '''InstructBlipForConditionalGeneration''',
		    '''InstructBlipVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    InstructBlipConfig,
		    InstructBlipQFormerConfig,
		    InstructBlipVisionConfig,
		)
		from .processing_instructblip import InstructBlipProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_instructblip import (
				    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    InstructBlipForConditionalGeneration,
				    InstructBlipPreTrainedModel,
				    InstructBlipQFormerModel,
				    InstructBlipVisionModel,
				)

else:
		import sys

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