


"""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    A_ ( _lowerCAmelCase  :       List[str],  _lowerCAmelCase  :       Optional[Any],  _lowerCAmelCase  :       Union[str, Any]							):






     """simple docstring"""

     _a     						=					FNetConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					FNetForPreTraining(_lowerCAmelCase							)

     _a     						=					restore_checkpoint(_lowerCAmelCase,  _lowerCAmelCase							)
     _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(_lowerCAmelCase							)

     # Save PreTrained
     print(f'Saving pretrained model to {save_path}'							)
     fnet_pretraining_model.save_pretrained(_lowerCAmelCase							)


if __name__ == "__main__":
   __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.''')
   __snake_case												=							parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       str							):






     """simple docstring"""

     return "".join(chr(ord(_lowerCAmelCase							) + 32							) if '''A''' <= char <= '''Z''' else char for char in word							)


if __name__ == "__main__":
   from doctest import testmod

   testmod()