


'''simple docstring'''
import argparse

import torch

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


logging.set_verbosity_info()


def 			a      (			__a ,					__a ,					__a   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= MobileBertConfig.from_json_file(__a   )
					print(f'''Building PyTorch model from configuration: {config}'''   )
					UpperCamelCase__   :List[str]												= MobileBertForPreTraining(__a   )
					# Load weights from tf checkpoint
					UpperCamelCase__   :str												= load_tf_weights_in_mobilebert(__a ,					__a ,					__a   )
					# Save pytorch-model
					print(f'''Save PyTorch model to {pytorch_dump_path}'''   )
					torch.save(model.state_dict() ,					__a   )


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

'''simple docstring'''




from typing import TYPE_CHECKING

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


lowercase			:				Dict    		=			{
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				List[Any]    		=			['Speech2TextTokenizer']

try:
      if not is_speech_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				List[str]    		=			['Speech2TextFeatureExtractor']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Dict    		=			[
          'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSpeech2TextForConditionalGeneration',
          'TFSpeech2TextModel',
          'TFSpeech2TextPreTrainedModel',
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Any    		=			[
          'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Speech2TextForConditionalGeneration',
          'Speech2TextModel',
          'Speech2TextPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
      from .processing_speech_to_text import SpeechaTextProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speech_to_text import SpeechaTextTokenizer

      try:
            if not is_speech_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_speech_to_text import (
                TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFSpeechaTextForConditionalGeneration,
                TFSpeechaTextModel,
                TFSpeechaTextPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text import (
                SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextForConditionalGeneration,
                SpeechaTextModel,
                SpeechaTextPreTrainedModel,
            )

else:
      import sys

      lowercase			:				List[Any]    		=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




