

import torch
from torch import autograd







class    UpperCamelCase_			(  autograd.Function			):


  """simple docstring"""



  @staticmethod
  def      _UpperCamelCase    (	A_    :				int     ,  A_    :				torch.tensor     ,  A_    :				float     ,  A_    :				bool	)					-> Optional[int]:
      SCREAMING_SNAKE_CASE__					     =						inputs.numel()
      SCREAMING_SNAKE_CASE__					     =						int(0.005 * nb_elems	) + 1
      if sigmoid:
          SCREAMING_SNAKE_CASE__					     =						(torch.sigmoid(A_	) > threshold).type(inputs.type()	)
      else:
          SCREAMING_SNAKE_CASE__					     =						(inputs > threshold).type(inputs.type()	)
      if mask.sum() < nb_min:
          # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
          SCREAMING_SNAKE_CASE__					     =						inputs.flatten().kthvalue(max(nb_elems - nb_min     ,  1	)	).values
          SCREAMING_SNAKE_CASE__					     =						(inputs > k_threshold).type(inputs.type()	)
      return mask







  @staticmethod
  def      _UpperCamelCase    (	A_    :				Optional[Any]     ,  A_    :				Any	)					-> Union[str, Any]:
      return gradOutput, None, None







class    UpperCamelCase_			(  autograd.Function			):


  """simple docstring"""



  @staticmethod
  def      _UpperCamelCase    (	A_    :				Optional[Any]     ,  A_    :				torch.tensor     ,  A_    :				float	)					-> int:
      SCREAMING_SNAKE_CASE__					     =						inputs.clone()
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						inputs.flatten().sort(descending=A_	)
      SCREAMING_SNAKE_CASE__					     =						int(threshold * inputs.numel()	)

      # flat_out and mask access the same memory.
      SCREAMING_SNAKE_CASE__					     =						mask.flatten()
      SCREAMING_SNAKE_CASE__					     =						0
      SCREAMING_SNAKE_CASE__					     =						1
      return mask







  @staticmethod
  def      _UpperCamelCase    (	A_    :				Any     ,  A_    :				Dict	)					-> Union[str, Any]:
      return gradOutput, None







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""



  @staticmethod
  def      _UpperCamelCase    (	A_    :				torch.tensor     ,  A_    :				float	)					-> Dict:
      SCREAMING_SNAKE_CASE__					     =						inputs.clone()
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						inputs.abs().flatten().sort(descending=A_	)
      SCREAMING_SNAKE_CASE__					     =						int(threshold * inputs.numel()	)

      # flat_out and mask access the same memory.
      SCREAMING_SNAKE_CASE__					     =						mask.flatten()
      SCREAMING_SNAKE_CASE__					     =						0
      SCREAMING_SNAKE_CASE__					     =						1
      return mask





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       (					__A ,       __A ,       __A	):
    # Initialise PyTorch model
    SCREAMING_SNAKE_CASE__					     =						FNetConfig.from_json_file(__A	)
    print(f"Building PyTorch model from configuration: {config}"	)
    SCREAMING_SNAKE_CASE__					     =						FNetForPreTraining(__A	)

    SCREAMING_SNAKE_CASE__					     =						restore_checkpoint(__A ,       __A	)
    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(__A	)

    # Save PreTrained
    print(f"Saving pretrained model to {save_path}"	)
    fnet_pretraining_model.save_pretrained(__A	)


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)


