code: 
  
  
  
  
  """simple docstring"""
  
  
  
  import argparse
  
  import torch
  from flax.training.checkpoints import restore_checkpoint
  
  from transformers import FNetConfig, FNetForPreTraining
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	List[str]:
   # Initialise PyTorch model
   A__ =       FNetConfig.from_json_file(lowercase_		)
   print(f"""Building PyTorch model from configuration: {config}"""		)
   A__ =       FNetForPreTraining(lowercase_		)
  
   A__ =       restore_checkpoint(lowercase_    ,				lowercase_		)
   A__ =       checkpoint_dict["target"]
  
   # Embeddings
   # Position IDs
   A__ =       fnet_pretraining_model.state_dict()
  
   A__ =       state_dict["fnet.embeddings.position_ids"]
   A__ =       {"fnet.embeddings.position_ids": position_ids}
   # Embedding Layers
   A__ =       torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
   A__ =       torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]		)
   A__ =       torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]		)
   A__ =       torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]		).T
   A__ =       torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]		)
   A__ =       torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]		)
   A__ =       torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]		)
  
   # Encoder Layers
   for layer in range(config.num_hidden_layers		):
    A__ =       torch.tensor(
        pretrained_model_params["encoder"][f"""encoder_{layer}"""]["mixing_layer_norm"]["scale"]		)
    A__ =       torch.tensor(
        pretrained_model_params["encoder"][f"""encoder_{layer}"""]["mixing_layer_norm"]["bias"]		)
  
    A__ =       torch.tensor(
        pretrained_model_params["encoder"][f"""feed_forward_{layer}"""]["intermediate"]["kernel"]		).T
    A__ =       torch.tensor(
        pretrained_model_params["encoder"][f"""feed_forward_{layer}"""]["intermediate"]["bias"]		)
  
    A__ =       torch.tensor(
        pretrained_model_params["encoder"][f"""feed_forward_{layer}"""]["output"]["kernel"]		).T
    A__ =       torch.tensor(
        pretrained_model_params["encoder"][f"""feed_forward_{layer}"""]["output"]["bias"]		)
  
    A__ =       torch.tensor(
        pretrained_model_params["encoder"][f"""encoder_{layer}"""]["output_layer_norm"]["scale"]		)
    A__ =       torch.tensor(
        pretrained_model_params["encoder"][f"""encoder_{layer}"""]["output_layer_norm"]["bias"]		)
  
   # Pooler Layers
   A__ =       torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]		).T
   A__ =       torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]		)
  
   # Masked LM Layers
   A__ =       torch.tensor(
       pretrained_model_params["predictions_dense"]["kernel"]		).T
   A__ =       torch.tensor(
       pretrained_model_params["predictions_dense"]["bias"]		)
   A__ =       torch.tensor(
       pretrained_model_params["predictions_layer_norm"]["scale"]		)
   A__ =       torch.tensor(
       pretrained_model_params["predictions_layer_norm"]["bias"]		)
   A__ =       torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
   A__ =       torch.tensor(
       pretrained_model_params["predictions_output"]["output_bias"]		)
   A__ =       torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]		)
  
   # Seq Relationship Layers
   A__ =       torch.tensor(
       pretrained_model_params["classification"]["output_kernel"]		)
   A__ =       torch.tensor(
       pretrained_model_params["classification"]["output_bias"]		)
  
   # Load State Dict
   fnet_pretraining_model.load_state_dict(lowercase_		)
  
   # Save PreTrained
   print(f"""Saving pretrained model to {save_path}"""		)
   fnet_pretraining_model.save_pretrained(lowercase_		)
  
  
  if __name__ == "__main__":
        SCREAMING_SNAKE_CASE       = argparse.ArgumentParser()
        # Required parameters
        parser.add_argument(
            "--flax_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
        )
        parser.add_argument(
            "--fnet_config_file",
            default=None,
            type=str,
            required=True,
            help=(
                "The config json file corresponding to the pre-trained FNet model. \n"
                "This specifies the model architecture."
            ),
        )
        parser.add_argument("--save_path", default=None, type=str, required=True, help="Path to the output model.")
        SCREAMING_SNAKE_CASE       = parser.parse_args()
        convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import _LazyModule
  
  
  SCREAMING_SNAKE_CASE       = {"tokenization_bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"]}
  
  
  if TYPE_CHECKING:
        from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE       = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  