





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase :			Any     		=    logging.get_logger(__name__)

lowercase :			int     		=    {
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}





class 					A__       (				UpperCAmelCase_      ):




   """simple docstring"""



   __A  :							Optional[Any]			    =    '''fnet'''

   def __init__(   self   ,    lowercase=3_2000   ,    lowercase=768   ,    lowercase=12   ,    lowercase=3072   ,    lowercase="gelu_new"   ,    lowercase=0.1   ,    lowercase=512   ,    lowercase=4   ,    lowercase=0.02   ,    lowercase=1e-12   ,    lowercase=False   ,    lowercase=512   ,    lowercase=3   ,    lowercase=1   ,    lowercase=2   ,    **lowercase   ,    )     ->		List[str]:







          '''simple docstring'''

          super().__init__(pad_token_id=__a   ,    bos_token_id=__a   ,    eos_token_id=__a   ,    **__a)

          a__		:      Any					=							vocab_size
          a__		:      str					=							max_position_embeddings
          a__		:      Dict					=							hidden_size
          a__		:      Tuple					=							num_hidden_layers
          a__		:      Dict					=							intermediate_size
          a__		:      List[Any]					=							hidden_act
          a__		:      List[Any]					=							hidden_dropout_prob
          a__		:      Optional[Any]					=							initializer_range
          a__		:      Tuple					=							type_vocab_size
          a__		:      Any					=							layer_norm_eps
          a__		:      int					=							use_tpu_fourier_optimizations
          a__		:      Optional[int]					=							tpu_short_seq_length







import argparse

import torch

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


logging.set_verbosity_info()

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		MobileBertConfig.from_json_file(UpperCAmelCase__			)
      print(F"""Building PyTorch model from configuration: {config}"""			)
      lowerCamelCase             =		MobileBertForPreTraining(UpperCAmelCase__			)
      # Load weights from tf checkpoint
      lowerCamelCase             =		load_tf_weights_in_mobilebert(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
      torch.save(model.state_dict() , UpperCAmelCase__			)


if __name__ == "__main__":
     a_    :  Any						  =		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.'
     )
     a_    :  List[Any]						  =		parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)