

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 decimal







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=0
     SCREAMING_SNAKE_CASE										=decimal.Context(prec=105      )
     for i in range(2,   100      ):
          SCREAMING_SNAKE_CASE										=decimal.Decimal(lowerCAmelCase_      )
          SCREAMING_SNAKE_CASE										=number.sqrt(lowerCAmelCase_      )
          if len(str(lowerCAmelCase_      )      ) > 1:
               answer += int(str(lowerCAmelCase_      )[0]      )
               SCREAMING_SNAKE_CASE										=str(lowerCAmelCase_      )[2:101]
               answer += sum(int(lowerCAmelCase_      ) for x in sqrt_number_str      )
     return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(f'{solution() = }')

