"""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__    (	lowercase_     :	Optional[Any]      , lowercase_     :	List[Any]      , lowercase_     :	Tuple						)  ->							Any:
   # Initialise PyTorch model
   __lowerCamelCase    		=					FNetConfig.from_json_file(A__						)
   print(F'''Building PyTorch model from configuration: {config}'''						)
   __lowerCamelCase    		=					FNetForPreTraining(A__						)

   __lowerCamelCase    		=					restore_checkpoint(A__      , A__						)
   __lowerCamelCase    		=					checkpoint_dict['target']

   # Embeddings
   # Position IDs
   __lowerCamelCase    		=					fnet_pretraining_model.state_dict()

   __lowerCamelCase    		=					state_dict['fnet.embeddings.position_ids']
   __lowerCamelCase    		=					{'fnet.embeddings.position_ids': position_ids}
   # Embedding Layers
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['encoder']['embedder']['word']['embedding']						)
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['encoder']['embedder']['position']['embedding'][0]						)
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['encoder']['embedder']['type']['embedding']						)
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']						).T
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']						)
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['encoder']['embedder']['layer_norm']['scale']						)
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['encoder']['embedder']['layer_norm']['bias']						)

   # Encoder Layers
   for layer in range(config.num_hidden_layers						):
      __lowerCamelCase    		=					torch.tensor(
          pretrained_model_params['encoder'][F'''encoder_{layer}''']['mixing_layer_norm']['scale']						)
      __lowerCamelCase    		=					torch.tensor(
          pretrained_model_params['encoder'][F'''encoder_{layer}''']['mixing_layer_norm']['bias']						)

      __lowerCamelCase    		=					torch.tensor(
          pretrained_model_params['encoder'][F'''feed_forward_{layer}''']['intermediate']['kernel']						).T
      __lowerCamelCase    		=					torch.tensor(
          pretrained_model_params['encoder'][F'''feed_forward_{layer}''']['intermediate']['bias']						)

      __lowerCamelCase    		=					torch.tensor(
          pretrained_model_params['encoder'][F'''feed_forward_{layer}''']['output']['kernel']						).T
      __lowerCamelCase    		=					torch.tensor(
          pretrained_model_params['encoder'][F'''feed_forward_{layer}''']['output']['bias']						)

      __lowerCamelCase    		=					torch.tensor(
          pretrained_model_params['encoder'][F'''encoder_{layer}''']['output_layer_norm']['scale']						)
      __lowerCamelCase    		=					torch.tensor(
          pretrained_model_params['encoder'][F'''encoder_{layer}''']['output_layer_norm']['bias']						)

   # Pooler Layers
   __lowerCamelCase    		=					torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']						).T
   __lowerCamelCase    		=					torch.tensor(pretrained_model_params['encoder']['pooler']['bias']						)

   # Masked LM Layers
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['predictions_dense']['kernel']						).T
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['predictions_dense']['bias']						)
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['predictions_layer_norm']['scale']						)
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['predictions_layer_norm']['bias']						)
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['encoder']['embedder']['word']['embedding']						)
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['predictions_output']['output_bias']						)
   __lowerCamelCase    		=					torch.tensor(pretrained_model_params['predictions_output']['output_bias']						)

   # Seq Relationship Layers
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['classification']['output_kernel']						)
   __lowerCamelCase    		=					torch.tensor(
       pretrained_model_params['classification']['output_bias']						)

   # Load State Dict
   fnet_pretraining_model.load_state_dict(A__						)

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


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   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	:		str   =   parser.parse_args()
     convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)







def 					UpperCamelCase__		(							A__ = 400_0000       )  ->   int:

				if n <= 1:
								return 0
				snake_case__   :      Optional[Any]   				= 0
				snake_case__   :      Optional[int]   				= 2
				snake_case__   :      str   				= 0
				while 4 * b + a <= n:
								snake_case__				,    snake_case__   :      Optional[Any]   				= b, 4 * b + a
								count += a
				return count + b


if __name__ == "__main__":
				print(F'''{solution() = }''')


