

import argparse

import torch

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


logging.set_verbosity_info()





def 	__a     (      A__			:  str			,  A__			:  Optional[int]			,  A__			:  int       ):
    # Initialise PyTorch model
    SCREAMING_SNAKE_CASE										=   AlbertConfig.from_json_file(A__       )
    print(F"Building PyTorch model from configuration: {config}"       )
    SCREAMING_SNAKE_CASE										=   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			:					List[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(
        '--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			:					Optional[Any]						=			parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase:	Optional[Any]       			=		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''RagModel''',
				    '''RagPreTrainedModel''',
				    '''RagSequenceForGeneration''',
				    '''RagTokenForGeneration''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''TFRagModel''',
				    '''TFRagPreTrainedModel''',
				    '''TFRagSequenceForGeneration''',
				    '''TFRagTokenForGeneration''',
				]


if TYPE_CHECKING:
				from .configuration_rag import RagConfig
				from .retrieval_rag import RagRetriever
				from .tokenization_rag import RagTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rag import (
								    TFRagModel,
								    TFRagPreTrainedModel,
								    TFRagSequenceForGeneration,
								    TFRagTokenForGeneration,
								)

else:
				import sys

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






