

"""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 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__      ) ->	Optional[Any]:
 # Initialise PyTorch model
 lowerCAmelCase__      :							Optional[int]    =							FNetConfig.from_json_file(lowercase__      )
 print(F"""Building PyTorch model from configuration: {config}"""      )
 lowerCAmelCase__      :							Tuple    =							FNetForPreTraining(lowercase__      )

 lowerCAmelCase__      :							Tuple    =							restore_checkpoint(lowercase__				,				lowercase__      )
 lowerCAmelCase__      :							str    =							checkpoint_dict["target"]

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

 lowerCAmelCase__      :							Optional[int]    =							state_dict["fnet.embeddings.position_ids"]
 lowerCAmelCase__      :							Optional[Any]    =							{"fnet.embeddings.position_ids": position_ids}
 # Embedding Layers
 lowerCAmelCase__      :							Dict    =							torch.tensor(
     pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]      )
 lowerCAmelCase__      :							Dict    =							torch.tensor(
     pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]      )
 lowerCAmelCase__      :							List[Any]    =							torch.tensor(
     pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]      )
 lowerCAmelCase__      :							Dict    =							torch.tensor(
     pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]      ).T
 lowerCAmelCase__      :							Tuple    =							torch.tensor(
     pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]      )
 lowerCAmelCase__      :							List[str]    =							torch.tensor(
     pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]      )
 lowerCAmelCase__      :							List[Any]    =							torch.tensor(
     pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]      )

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

  lowerCAmelCase__      :							Union[str, Any]    =							torch.tensor(
      pretrained_model_params["encoder"][F"""feed_forward_{layer}"""]["intermediate"]["kernel"]      ).T
  lowerCAmelCase__      :							Tuple    =							torch.tensor(
      pretrained_model_params["encoder"][F"""feed_forward_{layer}"""]["intermediate"]["bias"]      )

  lowerCAmelCase__      :							str    =							torch.tensor(
      pretrained_model_params["encoder"][F"""feed_forward_{layer}"""]["output"]["kernel"]      ).T
  lowerCAmelCase__      :							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__      :							List[str]    =							torch.tensor(
      pretrained_model_params["encoder"][F"""encoder_{layer}"""]["output_layer_norm"]["bias"]      )

 # Pooler Layers
 lowerCAmelCase__      :							Any    =							torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]      ).T
 lowerCAmelCase__      :							int    =							torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]      )

 # Masked LM Layers
 lowerCAmelCase__      :							str    =							torch.tensor(
     pretrained_model_params["predictions_dense"]["kernel"]      ).T
 lowerCAmelCase__      :							Union[str, Any]    =							torch.tensor(
     pretrained_model_params["predictions_dense"]["bias"]      )
 lowerCAmelCase__      :							int    =							torch.tensor(
     pretrained_model_params["predictions_layer_norm"]["scale"]      )
 lowerCAmelCase__      :							Dict    =							torch.tensor(
     pretrained_model_params["predictions_layer_norm"]["bias"]      )
 lowerCAmelCase__      :							Optional[int]    =							torch.tensor(
     pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]      )
 lowerCAmelCase__      :							Any    =							torch.tensor(
     pretrained_model_params["predictions_output"]["output_bias"]      )
 lowerCAmelCase__      :							Tuple    =							torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]      )

 # Seq Relationship Layers
 lowerCAmelCase__      :							Tuple    =							torch.tensor(
     pretrained_model_params["classification"]["output_kernel"]      )
 lowerCAmelCase__      :							Optional[int]    =							torch.tensor(
     pretrained_model_params["classification"]["output_bias"]      )

 # Load State Dict
 fnet_pretraining_model.load_state_dict(lowercase__      )

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


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)









"""simple docstring"""

def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 return 1 if input_a == 0 else 0



def 			SCREAMING_SNAKE_CASE       (   ) ->	None:
 assert not_gate(0      ) == 1
 assert not_gate(1      ) == 0


if __name__ == "__main__":
    print(not_gate(0))
    print(not_gate(1))






