



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
    from ..ta.tokenization_ta import TaTokenizer
else:
    from ...utils.dummy_sentencepiece_objects import TaTokenizer

__UpperCAmelCase          =       TaTokenizer

if is_tokenizers_available():
    from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
    from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__UpperCAmelCase          =       TaTokenizerFast

__UpperCAmelCase          =       {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase          =       [
        '''MT5EncoderModel''',
        '''MT5ForConditionalGeneration''',
        '''MT5ForQuestionAnswering''',
        '''MT5Model''',
        '''MT5PreTrainedModel''',
        '''MT5Stack''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase          =       ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase          =       ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
    from .configuration_mta import MTaConfig, MTaOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mta import (
            MTaEncoderModel,
            MTaForConditionalGeneration,
            MTaForQuestionAnswering,
            MTaModel,
            MTaPreTrainedModel,
            MTaStack,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
    import sys

    __UpperCAmelCase          =       _LazyModule(
        __name__,
        globals()['''__file__'''],
        _import_structure,
        extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
        module_spec=__spec__,
    )








import argparse

import torch

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


logging.set_verbosity_info()
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   # Initialise PyTorch model
   UpperCamelCase    :  Any							=	MobileBertConfig.from_json_file(snake_case__	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   UpperCamelCase    :  Tuple							=	MobileBertForPreTraining(snake_case__	)
   # Load weights from tf checkpoint
   UpperCamelCase    :  int							=	load_tf_weights_in_mobilebert(snake_case__       ,     snake_case__       ,     snake_case__	)
   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   torch.save(model.state_dict()       ,     snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       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.'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



