






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       (    SCREAMING_SNAKE_CASE__  :     Dict					,  SCREAMING_SNAKE_CASE__  :     Optional[int]					,  SCREAMING_SNAKE_CASE__  :     Any	):
    # Initialise PyTorch model
    UpperCamelCase	:Tuple     							=       FNetConfig.from_json_file(SCREAMING_SNAKE_CASE__	)
    print(F'''Building PyTorch model from configuration: {config}'''	)
    UpperCamelCase	:List[str]     							=       FNetForPreTraining(SCREAMING_SNAKE_CASE__	)

    UpperCamelCase	:Dict     							=       restore_checkpoint(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Tuple     							=       checkpoint_dict['''target''']

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

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

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

        UpperCamelCase	:int     							=       torch.tensor(
            pretrained_model_params['''encoder'''][F'''feed_forward_{layer}''']['''intermediate''']['''kernel''']	).T
        UpperCamelCase	:Union[str, Any]     							=       torch.tensor(
            pretrained_model_params['''encoder'''][F'''feed_forward_{layer}''']['''intermediate''']['''bias''']	)

        UpperCamelCase	:Any     							=       torch.tensor(
            pretrained_model_params['''encoder'''][F'''feed_forward_{layer}''']['''output''']['''kernel''']	).T
        UpperCamelCase	:Dict     							=       torch.tensor(
            pretrained_model_params['''encoder'''][F'''feed_forward_{layer}''']['''output''']['''bias''']	)

        UpperCamelCase	:Tuple     							=       torch.tensor(
            pretrained_model_params['''encoder'''][F'''encoder_{layer}''']['''output_layer_norm''']['''scale''']	)
        UpperCamelCase	:Dict     							=       torch.tensor(
            pretrained_model_params['''encoder'''][F'''encoder_{layer}''']['''output_layer_norm''']['''bias''']	)

    # Pooler Layers
    UpperCamelCase	:List[str]     							=       torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']	).T
    UpperCamelCase	:int     							=       torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']	)

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

    # Seq Relationship Layers
    UpperCamelCase	:Any     							=       torch.tensor(
        pretrained_model_params['''classification''']['''output_kernel''']	)
    UpperCamelCase	:List[Any]     							=       torch.tensor(
        pretrained_model_params['''classification''']['''output_bias''']	)

    # Load State Dict
    fnet_pretraining_model.load_state_dict(SCREAMING_SNAKE_CASE__	)

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


if __name__ == "__main__":
 __snake_case							 =  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.""")
 __snake_case							 =  parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)













def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     float	):
    UpperCamelCase	:Tuple     							=       float()
    try:
        UpperCamelCase	:Union[str, Any]     							=       mass * acceleration
    except Exception:
        return -0.0
    return force


if __name__ == "__main__":
 import doctest

 # run doctest
 doctest.testmod()

 # demo
 __snake_case							 =  1_2.5
 __snake_case							 =  10
 __snake_case							 =  newtons_second_law_of_motion(mass, acceleration)
 print("""The force is """, force, """N""")





