

"""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__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=FNetConfig.from_json_file(lowerCAmelCase_      )
     print(F'Building PyTorch model from configuration: {config}'      )
     SCREAMING_SNAKE_CASE										=FNetForPreTraining(lowerCAmelCase_      )

     SCREAMING_SNAKE_CASE										=restore_checkpoint(lowerCAmelCase_,   lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=checkpoint_dict['target']

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

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

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

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

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

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

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

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

     # Seq Relationship Layers
     SCREAMING_SNAKE_CASE										=torch.tensor(
         pretrained_model_params['classification']['output_kernel']      )
     SCREAMING_SNAKE_CASE										=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__":
 _lowerCamelCase								=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.")
 _lowerCamelCase								=parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




import random
import sys

_lowerCamelCase								="ABCDEFGHIJKLMNOPQRSTUVWXYZ"







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=input('Enter message: '      )
     SCREAMING_SNAKE_CASE										='LFWOAYUISVKMNXPBDCRJTQEGHZ'
     SCREAMING_SNAKE_CASE										=input('Encrypt/Decrypt [e/d]: '      )

     check_valid_key(lowerCAmelCase_      )

     if resp.lower().startswith('e'      ):
          SCREAMING_SNAKE_CASE										='encrypt'
          SCREAMING_SNAKE_CASE										=encrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )
     elif resp.lower().startswith('d'      ):
          SCREAMING_SNAKE_CASE										='decrypt'
          SCREAMING_SNAKE_CASE										=decrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )

     print(F'\n{mode.title()}ion: \n{translated}'      )







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=list(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=list(lowerCAmelCase_      )
     key_list.sort()
     letters_list.sort()

     if key_list != letters_list:
          sys.exit('Error in the key or symbol set.'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     return translate_message(lowerCAmelCase_,   lowerCAmelCase_,   'encrypt'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     return translate_message(lowerCAmelCase_,   lowerCAmelCase_,   'decrypt'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=''
     SCREAMING_SNAKE_CASE										=LETTERS
     SCREAMING_SNAKE_CASE										=key

     if mode == "decrypt":
          SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=chars_b, chars_a

     for symbol in message:
          if symbol.upper() in chars_a:
               SCREAMING_SNAKE_CASE										=chars_a.find(symbol.upper()      )
               if symbol.isupper():
                    translated += chars_b[sym_index].upper()
               else:
                    translated += chars_b[sym_index].lower()
          else:
               translated += symbol

     return translated







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=list(lowerCAmelCase_      )
     random.shuffle(lowerCAmelCase_      )
     return "".join(lowerCAmelCase_      )


if __name__ == "__main__":
 main()

