




from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

UpperCAmelCase_   							=      TypeVar('T', bound=bool)





class        lowerCamelCase__(   Generic[T]):
      def __init__(							self:					List[Any]       ,			UpperCamelCase_:					T  ):
             __lowerCamelCase  =		value
             __lowerCamelCase  =		None
             __lowerCamelCase  =		None
      @property
      def      lowerCAmelCase__		(							self:					List[str]  ):
             return self._value




      @staticmethod
      def      lowerCAmelCase__		(							UpperCamelCase_:					SkewNode[T] | None       ,			UpperCamelCase_:					SkewNode[T] | None  ):
             if not roota:
                    return roota

             if not roota:
                    return roota

             if roota.value > roota.value:
                    __lowerCamelCase,  __lowerCamelCase  =		roota, roota

             __lowerCamelCase  =		roota
             __lowerCamelCase  =		roota.right
             __lowerCamelCase  =		roota.left
             __lowerCamelCase  =		SkewNode.merge(UpperCamelCase_       ,			UpperCamelCase_  )

             return result





class        lowerCamelCase__(   Generic[T]):
      def __init__(							self:					Dict       ,			UpperCamelCase_:					Iterable[T] | None = ()  ):
             __lowerCamelCase  =		None
             if data:
                    for item in data:
                           self.insert(UpperCamelCase_  )
      def __bool__(							self:					Any  ):
             return self._root is not None
      def __iter__(							self:					int  ):
             __lowerCamelCase  =		[]
             while self:
                    result.append(self.pop()  )

             # Pushing items back to the heap not to clear it.
             for item in result:
                    self.insert(UpperCamelCase_  )

             return iter(UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Dict       ,			UpperCamelCase_:					T  ):
             __lowerCamelCase  =		SkewNode.merge(self._root       ,			SkewNode(UpperCamelCase_  )  )
      def      lowerCAmelCase__		(							self:					List[str]  ):
             __lowerCamelCase  =		self.top()
             __lowerCamelCase  =		(
                 SkewNode.merge(self._root.left       ,			self._root.right  ) if self._root else None
             )

             return result
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             if not self._root:
                    raise IndexError("""Can't get top element for the empty heap."""  )
             return self._root.value




      def      lowerCAmelCase__		(							self:					Tuple  ):
             __lowerCamelCase  =		None


if __name__ == "__main__":
       import doctest

       doctest.testmod()







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    lowerCamelCase__			(		A__			:   int    ,     A__			:   Tuple    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		FNetConfig.from_json_file(A__      )
       print(f'Building PyTorch model from configuration: {config}'      )
       __lowerCamelCase  =		FNetForPreTraining(A__      )

       __lowerCamelCase  =		restore_checkpoint(A__    ,     A__      )
       __lowerCamelCase  =		checkpoint_dict["""target"""]

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

       __lowerCamelCase  =		state_dict["""fnet.embeddings.position_ids"""]
       __lowerCamelCase  =		{"""fnet.embeddings.position_ids""": position_ids}
       # Embedding Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]      )

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

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""intermediate"""]["""kernel"""]      ).T
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""intermediate"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""output"""]["""kernel"""]      ).T
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""output"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""output_layer_norm"""]["""scale"""]      )
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""output_layer_norm"""]["""bias"""]      )

       # Pooler Layers
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]      )

       # Masked LM Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_dense"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_dense"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_layer_norm"""]["""scale"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_layer_norm"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_output"""]["""output_bias"""]      )
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]      )

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

