
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       :  str	,__UpperCamelCase       :  Dict	,__UpperCamelCase       :  int    ):
   # Initialise PyTorch model
   lowerCAmelCase__ :    List[Any]				      =			FNetConfig.from_json_file(__UpperCamelCase    )
   print(f'''Building PyTorch model from configuration: {config}'''    )
   lowerCAmelCase__ :    List[Any]				      =			FNetForPreTraining(__UpperCamelCase    )

   lowerCAmelCase__ :    Optional[Any]				      =			restore_checkpoint(__UpperCamelCase	,__UpperCamelCase    )
   lowerCAmelCase__ :    str				      =			checkpoint_dict['''target''']

   # Embeddings
   # Position IDs
   lowerCAmelCase__ :    int				      =			fnet_pretraining_model.state_dict()

   lowerCAmelCase__ :    List[Any]				      =			state_dict['''fnet.embeddings.position_ids''']
   lowerCAmelCase__ :    Tuple				      =			{'''fnet.embeddings.position_ids''': position_ids}
   # Embedding Layers
   lowerCAmelCase__ :    List[str]				      =			torch.tensor(
       pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']    )
   lowerCAmelCase__ :    Any				      =			torch.tensor(
       pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]    )
   lowerCAmelCase__ :    Tuple				      =			torch.tensor(
       pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']    )
   lowerCAmelCase__ :    Union[str, Any]				      =			torch.tensor(
       pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']    ).T
   lowerCAmelCase__ :    Optional[Any]				      =			torch.tensor(
       pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']    )
   lowerCAmelCase__ :    Tuple				      =			torch.tensor(
       pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']    )
   lowerCAmelCase__ :    Union[str, Any]				      =			torch.tensor(
       pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']    )

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

      lowerCAmelCase__ :    Dict				      =			torch.tensor(
          pretrained_model_params['''encoder'''][f'''feed_forward_{layer}''']['''intermediate''']['''kernel''']    ).T
      lowerCAmelCase__ :    Optional[int]				      =			torch.tensor(
          pretrained_model_params['''encoder'''][f'''feed_forward_{layer}''']['''intermediate''']['''bias''']    )

      lowerCAmelCase__ :    List[str]				      =			torch.tensor(
          pretrained_model_params['''encoder'''][f'''feed_forward_{layer}''']['''output''']['''kernel''']    ).T
      lowerCAmelCase__ :    List[str]				      =			torch.tensor(
          pretrained_model_params['''encoder'''][f'''feed_forward_{layer}''']['''output''']['''bias''']    )

      lowerCAmelCase__ :    Optional[int]				      =			torch.tensor(
          pretrained_model_params['''encoder'''][f'''encoder_{layer}''']['''output_layer_norm''']['''scale''']    )
      lowerCAmelCase__ :    int				      =			torch.tensor(
          pretrained_model_params['''encoder'''][f'''encoder_{layer}''']['''output_layer_norm''']['''bias''']    )

   # Pooler Layers
   lowerCAmelCase__ :    Optional[Any]				      =			torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']    ).T
   lowerCAmelCase__ :    Optional[int]				      =			torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']    )

   # Masked LM Layers
   lowerCAmelCase__ :    Optional[int]				      =			torch.tensor(
       pretrained_model_params['''predictions_dense''']['''kernel''']    ).T
   lowerCAmelCase__ :    Optional[Any]				      =			torch.tensor(
       pretrained_model_params['''predictions_dense''']['''bias''']    )
   lowerCAmelCase__ :    str				      =			torch.tensor(
       pretrained_model_params['''predictions_layer_norm''']['''scale''']    )
   lowerCAmelCase__ :    Union[str, Any]				      =			torch.tensor(
       pretrained_model_params['''predictions_layer_norm''']['''bias''']    )
   lowerCAmelCase__ :    str				      =			torch.tensor(
       pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']    )
   lowerCAmelCase__ :    int				      =			torch.tensor(
       pretrained_model_params['''predictions_output''']['''output_bias''']    )
   lowerCAmelCase__ :    Union[str, Any]				      =			torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias''']    )

   # Seq Relationship Layers
   lowerCAmelCase__ :    Union[str, Any]				      =			torch.tensor(
       pretrained_model_params['''classification''']['''output_kernel''']    )
   lowerCAmelCase__ :    Optional[Any]				      =			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__":
       A__       :   Tuple   						=					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__       :   Dict   						=					parser.parse_args()
       convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


A__       :   Tuple   						=					TypeVar("""T""", bound="""TaskTemplate""")





@dataclass(frozen=__UpperCamelCase					)
class 						lowercase       (  abc.ABC					):
					# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
					__a					=    42
					__a					=    42
					__a					=    42
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								return copy.deepcopy(self    )
					@property
					def lowercase_			(		self    ):
								"""simple docstring"""







								return Features(**self.input_schema ,	**self.label_schema    )
					@property
					@abc.abstractmethod
					def lowercase_			(		self    ):
								"""simple docstring"""







								raise NotImplementedError
					@classmethod
					def lowercase_			(		cls ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			{f.name for f in dataclasses.fields(cls    )}
								return cls(**{k: v for k, v in template_dict.items() if k in field_names}    )



