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 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Optional[Any]	,   __snake_case       :			Optional[int]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				int      =		FNetConfig.from_json_file(__snake_case      )
     print(F'Building PyTorch model from configuration: {config}'      )
     _lowerCamelCase  :				Optional[Any]      =		FNetForPreTraining(__snake_case      )
  
     _lowerCamelCase  :				Tuple      =		restore_checkpoint(__snake_case	,   __snake_case      )
     _lowerCamelCase  :				List[Any]      =		checkpoint_dict["""target"""]
  
     # Embeddings
     # Position IDs
     _lowerCamelCase  :				Optional[int]      =		fnet_pretraining_model.state_dict()
  
     _lowerCamelCase  :				Dict      =		state_dict["""fnet.embeddings.position_ids"""]
     _lowerCamelCase  :				Dict      =		{"""fnet.embeddings.position_ids""": position_ids}
     # Embedding Layers
     _lowerCamelCase  :				Optional[Any]      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
     _lowerCamelCase  :				Optional[int]      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]      )
     _lowerCamelCase  :				str      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]      )
     _lowerCamelCase  :				Dict      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]      ).T
     _lowerCamelCase  :				List[str]      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]      )
     _lowerCamelCase  :				str      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]      )
     _lowerCamelCase  :				str      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]      )
  
     # Encoder Layers
     for layer in range(config.num_hidden_layers      ):
        _lowerCamelCase  :				int      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'encoder_{layer}']["""mixing_layer_norm"""]["""scale"""]      )
        _lowerCamelCase  :				Optional[Any]      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'encoder_{layer}']["""mixing_layer_norm"""]["""bias"""]      )
  
        _lowerCamelCase  :				Union[str, Any]      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'feed_forward_{layer}']["""intermediate"""]["""kernel"""]      ).T
        _lowerCamelCase  :				Union[str, Any]      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'feed_forward_{layer}']["""intermediate"""]["""bias"""]      )
  
        _lowerCamelCase  :				List[str]      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'feed_forward_{layer}']["""output"""]["""kernel"""]      ).T
        _lowerCamelCase  :				List[str]      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'feed_forward_{layer}']["""output"""]["""bias"""]      )
  
        _lowerCamelCase  :				str      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'encoder_{layer}']["""output_layer_norm"""]["""scale"""]      )
        _lowerCamelCase  :				Dict      =		torch.tensor(
            pretrained_model_params["""encoder"""][F'encoder_{layer}']["""output_layer_norm"""]["""bias"""]      )
  
     # Pooler Layers
     _lowerCamelCase  :				Union[str, Any]      =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]      ).T
     _lowerCamelCase  :				str      =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]      )
  
     # Masked LM Layers
     _lowerCamelCase  :				Dict      =		torch.tensor(
         pretrained_model_params["""predictions_dense"""]["""kernel"""]      ).T
     _lowerCamelCase  :				List[str]      =		torch.tensor(
         pretrained_model_params["""predictions_dense"""]["""bias"""]      )
     _lowerCamelCase  :				Tuple      =		torch.tensor(
         pretrained_model_params["""predictions_layer_norm"""]["""scale"""]      )
     _lowerCamelCase  :				Optional[Any]      =		torch.tensor(
         pretrained_model_params["""predictions_layer_norm"""]["""bias"""]      )
     _lowerCamelCase  :				Optional[Any]      =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
     _lowerCamelCase  :				Tuple      =		torch.tensor(
         pretrained_model_params["""predictions_output"""]["""output_bias"""]      )
     _lowerCamelCase  :				int      =		torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]      )
  
     # Seq Relationship Layers
     _lowerCamelCase  :				Tuple      =		torch.tensor(
         pretrained_model_params["""classification"""]["""output_kernel"""]      )
     _lowerCamelCase  :				int      =		torch.tensor(
         pretrained_model_params["""classification"""]["""output_bias"""]      )
  
     # Load State Dict
     fnet_pretraining_model.load_state_dict(__snake_case      )
  
     # Save PreTrained
     print(F'Saving pretrained model to {save_path}'      )
     fnet_pretraining_model.save_pretrained(__snake_case      )
  
  
  if __name__ == "__main__":
   UpperCAmelCase   =	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.""")
   UpperCAmelCase   =	parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  from itertools import permutations
  
  def 							_snake_case			(      __snake_case       :			int = 5      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if gon_side < 3 or gon_side > 5:
        raise ValueError("""gon_side must be in the range [3, 5]"""      )
  
     # Since it's 16, we know 10 is on the outer ring
     # Put the big numbers at the end so that they are never the first number
     _lowerCamelCase  :				Dict      =		list(range(gon_side + 1	,   0	,   -1      )      )
     _lowerCamelCase  :				Optional[int]      =		list(range(gon_side + 2	,   gon_side * 2 + 1      )      )
  
     for perm in permutations(small_numbers + big_numbers      ):
        _lowerCamelCase  :				List[str]      =		generate_gon_ring(__snake_case	,   list(__snake_case      )      )
        if is_magic_gon(__snake_case      ):
           return int("""""".join(str(__snake_case      ) for n in numbers      )      )
  
     _lowerCamelCase  :				Union[str, Any]      =		F'Magic {gon_side}-gon ring is impossible'
     raise ValueError(__snake_case      )
  
  def 							_snake_case			(      __snake_case       :			int	,   __snake_case       :			list[int]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Union[str, Any]      =		[0] * (gon_side * 3)
     _lowerCamelCase  :				Dict      =		perm[0:3]
     perm.append(perm[1]      )
  
     _lowerCamelCase  :				Tuple      =		1 if gon_side < 5 else 2
  
     for i in range(1	,   len(__snake_case      ) // 3 + magic_number      ):
        _lowerCamelCase  :				Dict      =		perm[2 * i + 1]
        _lowerCamelCase  :				str      =		result[3 * i - 1]
        _lowerCamelCase  :				Dict      =		perm[2 * i + 2]
  
     return result
  
  def 							_snake_case			(      __snake_case       :			list[int]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if len(__snake_case      ) % 3 != 0:
        raise ValueError("""a gon ring should have a length that is a multiple of 3"""      )
  
     if min(numbers[::3]      ) != numbers[0]:
        return False
  
     _lowerCamelCase  :				str      =		sum(numbers[:3]      )
  
     return all(sum(numbers[i : i + 3]      ) == total for i in range(3	,   len(__snake_case      )	,   3      )      )
  
  
  if __name__ == "__main__":
   print(solution())
  
  