import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       # Initialise PyTorch model
       lowerCAmelCase__					     = MobileBertConfig.from_json_file(lowerCAmelCase__     )
       print(F"""Building PyTorch model from configuration: {config}"""     )
       lowerCAmelCase__					     = MobileBertForPreTraining(lowerCAmelCase__     )
       # Load weights from tf checkpoint
       lowerCAmelCase__					     = load_tf_weights_in_mobilebert(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(
        '--mobilebert_config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained MobileBERT 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.mobilebert_config_file, args.pytorch_dump_path)

from typing import TYPE_CHECKING

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


lowerCAmelCase__     						=			{
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'AdaptiveEmbedding',
        'TransfoXLForSequenceClassification',
        'TransfoXLLMHeadModel',
        'TransfoXLModel',
        'TransfoXLPreTrainedModel',
        'load_tf_weights_in_transfo_xl',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFAdaptiveEmbedding',
        'TFTransfoXLForSequenceClassification',
        'TFTransfoXLLMHeadModel',
        'TFTransfoXLMainLayer',
        'TFTransfoXLModel',
        'TFTransfoXLPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
    from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_transfo_xl import (
            TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            AdaptiveEmbedding,
            TransfoXLForSequenceClassification,
            TransfoXLLMHeadModel,
            TransfoXLModel,
            TransfoXLPreTrainedModel,
            load_tf_weights_in_transfo_xl,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_transfo_xl import (
            TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAdaptiveEmbedding,
            TFTransfoXLForSequenceClassification,
            TFTransfoXLLMHeadModel,
            TFTransfoXLMainLayer,
            TFTransfoXLModel,
            TFTransfoXLPreTrainedModel,
        )

else:
    import sys

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