



"""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__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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

  _SCREAMING_SNAKE_CASE        =			restore_checkpoint(UpperCamelCase__       ,							UpperCamelCase__    )
  _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(UpperCamelCase__    )

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


if __name__ == "__main__":
    lowerCamelCase				:     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.""")
    lowerCamelCase				:     Optional[int]								=  parser.parse_args()
    convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





"""simple docstring"""






import math


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  if not isinstance(UpperCamelCase__       ,							UpperCamelCase__    ):
    _SCREAMING_SNAKE_CASE        =			F'''Input value of [number={number}] must be an integer'''
    raise TypeError(UpperCamelCase__    )
  if number < 1:
    _SCREAMING_SNAKE_CASE        =			F'''Input value of [number={number}] must be a positive integer'''
    raise ValueError(UpperCamelCase__    )
  _SCREAMING_SNAKE_CASE        =			[number]
  while number != 1:
    if number % 2 == 0:
      _SCREAMING_SNAKE_CASE        =			math.floor(math.sqrt(UpperCamelCase__    )    )
    else:
      _SCREAMING_SNAKE_CASE        =			math.floor(
          math.sqrt(UpperCamelCase__    ) * math.sqrt(UpperCamelCase__    ) * math.sqrt(UpperCamelCase__    )    )
    sequence.append(UpperCamelCase__    )
  return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()
