code: 
  
  
  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		(		_lowercase				,     _lowercase				,     _lowercase   )				->   int:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = FNetConfig.from_json_file(_lowercase   )
       print(f'''Building PyTorch model from configuration: {config}'''   )
       __UpperCamelCase   = FNetForPreTraining(_lowercase   )
  
       __UpperCamelCase   = restore_checkpoint(_lowercase				,     _lowercase   )
       __UpperCamelCase   = checkpoint_dict['target']
  
       # Embeddings
       # Position IDs
       __UpperCamelCase   = fnet_pretraining_model.state_dict()
  
       __UpperCamelCase   = state_dict['fnet.embeddings.position_ids']
       __UpperCamelCase   = {'fnet.embeddings.position_ids': position_ids}
       # Embedding Layers
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['encoder']['embedder']['word']['embedding']   )
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['encoder']['embedder']['position']['embedding'][0]   )
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['encoder']['embedder']['type']['embedding']   )
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']   ).T
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']   )
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['encoder']['embedder']['layer_norm']['scale']   )
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['encoder']['embedder']['layer_norm']['bias']   )
  
       # Encoder Layers
       for layer in range(config.num_hidden_layers   ):
            __UpperCamelCase   = torch.tensor(
                pretrained_model_params['encoder'][f'''encoder_{layer}''']['mixing_layer_norm']['scale']   )
            __UpperCamelCase   = torch.tensor(
                pretrained_model_params['encoder'][f'''encoder_{layer}''']['mixing_layer_norm']['bias']   )
  
            __UpperCamelCase   = torch.tensor(
                pretrained_model_params['encoder'][f'''feed_forward_{layer}''']['intermediate']['kernel']   ).T
            __UpperCamelCase   = torch.tensor(
                pretrained_model_params['encoder'][f'''feed_forward_{layer}''']['intermediate']['bias']   )
  
            __UpperCamelCase   = torch.tensor(
                pretrained_model_params['encoder'][f'''feed_forward_{layer}''']['output']['kernel']   ).T
            __UpperCamelCase   = torch.tensor(
                pretrained_model_params['encoder'][f'''feed_forward_{layer}''']['output']['bias']   )
  
            __UpperCamelCase   = torch.tensor(
                pretrained_model_params['encoder'][f'''encoder_{layer}''']['output_layer_norm']['scale']   )
            __UpperCamelCase   = torch.tensor(
                pretrained_model_params['encoder'][f'''encoder_{layer}''']['output_layer_norm']['bias']   )
  
       # Pooler Layers
       __UpperCamelCase   = torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']   ).T
       __UpperCamelCase   = torch.tensor(pretrained_model_params['encoder']['pooler']['bias']   )
  
       # Masked LM Layers
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['predictions_dense']['kernel']   ).T
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['predictions_dense']['bias']   )
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['predictions_layer_norm']['scale']   )
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['predictions_layer_norm']['bias']   )
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['encoder']['embedder']['word']['embedding']   )
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['predictions_output']['output_bias']   )
       __UpperCamelCase   = torch.tensor(pretrained_model_params['predictions_output']['output_bias']   )
  
       # Seq Relationship Layers
       __UpperCamelCase   = torch.tensor(
           pretrained_model_params['classification']['output_kernel']   )
       __UpperCamelCase   = 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__":
      __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)
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   int | float:
  
  
       """simple docstring"""
  
  
       if len(_lowercase   ) == 0:
            raise ValueError('find_max() arg is an empty sequence'   )
       __UpperCamelCase   = nums[0]
       for x in nums:
            if x > max_num:
                 __UpperCamelCase   = x
       return max_num
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod(verbose=True)
  
  
  