

"""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       UpperCamelCase_					(     lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					Optional[Any]     , lowerCamelCase							:					str   )						->     Union[str, Any]:



       """simple docstring"""







       __magic_name__			:		Optional[int]        =     FNetConfig.from_json_file(lowerCamelCase   )
       print(f"""Building PyTorch model from configuration: {config}"""   )
       __magic_name__			:		int        =     FNetForPreTraining(lowerCamelCase   )

       __magic_name__			:		Optional[int]        =     restore_checkpoint(lowerCamelCase     , lowerCamelCase   )
       __magic_name__			:		int        =     checkpoint_dict['''target''']

       # Embeddings
       # Position IDs
       __magic_name__			:		int        =     fnet_pretraining_model.state_dict()

       __magic_name__			:		Optional[Any]        =     state_dict['''fnet.embeddings.position_ids''']
       __magic_name__			:		List[str]        =     {'''fnet.embeddings.position_ids''': position_ids}
       # Embedding Layers
       __magic_name__			:		Union[str, Any]        =     torch.tensor(
           pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']   )
       __magic_name__			:		Union[str, Any]        =     torch.tensor(
           pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]   )
       __magic_name__			:		int        =     torch.tensor(
           pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']   )
       __magic_name__			:		List[Any]        =     torch.tensor(
           pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']   ).T
       __magic_name__			:		Union[str, Any]        =     torch.tensor(
           pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']   )
       __magic_name__			:		Any        =     torch.tensor(
           pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']   )
       __magic_name__			:		Dict        =     torch.tensor(
           pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']   )

       # Encoder Layers
       for layer in range(config.num_hidden_layers   ):
              __magic_name__			:		Tuple        =     torch.tensor(
                  pretrained_model_params['''encoder'''][f"""encoder_{layer}"""]['''mixing_layer_norm''']['''scale''']   )
              __magic_name__			:		Dict        =     torch.tensor(
                  pretrained_model_params['''encoder'''][f"""encoder_{layer}"""]['''mixing_layer_norm''']['''bias''']   )

              __magic_name__			:		List[str]        =     torch.tensor(
                  pretrained_model_params['''encoder'''][f"""feed_forward_{layer}"""]['''intermediate''']['''kernel''']   ).T
              __magic_name__			:		List[Any]        =     torch.tensor(
                  pretrained_model_params['''encoder'''][f"""feed_forward_{layer}"""]['''intermediate''']['''bias''']   )

              __magic_name__			:		str        =     torch.tensor(
                  pretrained_model_params['''encoder'''][f"""feed_forward_{layer}"""]['''output''']['''kernel''']   ).T
              __magic_name__			:		Optional[Any]        =     torch.tensor(
                  pretrained_model_params['''encoder'''][f"""feed_forward_{layer}"""]['''output''']['''bias''']   )

              __magic_name__			:		List[Any]        =     torch.tensor(
                  pretrained_model_params['''encoder'''][f"""encoder_{layer}"""]['''output_layer_norm''']['''scale''']   )
              __magic_name__			:		Union[str, Any]        =     torch.tensor(
                  pretrained_model_params['''encoder'''][f"""encoder_{layer}"""]['''output_layer_norm''']['''bias''']   )

       # Pooler Layers
       __magic_name__			:		Union[str, Any]        =     torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']   ).T
       __magic_name__			:		Tuple        =     torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']   )

       # Masked LM Layers
       __magic_name__			:		str        =     torch.tensor(
           pretrained_model_params['''predictions_dense''']['''kernel''']   ).T
       __magic_name__			:		List[str]        =     torch.tensor(
           pretrained_model_params['''predictions_dense''']['''bias''']   )
       __magic_name__			:		Dict        =     torch.tensor(
           pretrained_model_params['''predictions_layer_norm''']['''scale''']   )
       __magic_name__			:		Dict        =     torch.tensor(
           pretrained_model_params['''predictions_layer_norm''']['''bias''']   )
       __magic_name__			:		List[str]        =     torch.tensor(
           pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']   )
       __magic_name__			:		Optional[int]        =     torch.tensor(
           pretrained_model_params['''predictions_output''']['''output_bias''']   )
       __magic_name__			:		Optional[int]        =     torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias''']   )

       # Seq Relationship Layers
       __magic_name__			:		Optional[Any]        =     torch.tensor(
           pretrained_model_params['''classification''']['''output_kernel''']   )
       __magic_name__			:		List[str]        =     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__":
 A									=						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.""")
 A									=						parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



"""simple docstring"""
def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     str:



       """simple docstring"""







       return " ".join([word[::-1] for word in input_str.split()]   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
