

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(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Dict:



    """simple docstring"""






    UpperCamelCase_      =	FNetConfig.from_json_file(SCREAMING_SNAKE_CASE_ )
    print(f"Building PyTorch model from configuration: {config}" )
    UpperCamelCase_      =	FNetForPreTraining(SCREAMING_SNAKE_CASE_ )

    UpperCamelCase_      =	restore_checkpoint(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )
    UpperCamelCase_      =	checkpoint_dict["target"]

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

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

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

        UpperCamelCase_      =	torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["kernel"] ).T
        UpperCamelCase_      =	torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["bias"] )

        UpperCamelCase_      =	torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["kernel"] ).T
        UpperCamelCase_      =	torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["bias"] )

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

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

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

    # Seq Relationship Layers
    UpperCamelCase_      =	torch.tensor(
        pretrained_model_params["classification"]["output_kernel"] )
    UpperCamelCase_      =	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__":
 SCREAMING_SNAKE_CASE		:int				  =    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.""")
 SCREAMING_SNAKE_CASE		:List[Any]				  =    parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	float:



    """simple docstring"""






    return -num if num < 0 else num




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    if len(SCREAMING_SNAKE_CASE_ ) == 0:
        raise ValueError("abs_min() arg is an empty sequence" )
    UpperCamelCase_      =	x[0]
    for i in x:
        if abs_val(SCREAMING_SNAKE_CASE_ ) < abs_val(SCREAMING_SNAKE_CASE_ ):
            UpperCamelCase_      =	i
    return j




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    if len(SCREAMING_SNAKE_CASE_ ) == 0:
        raise ValueError("abs_max() arg is an empty sequence" )
    UpperCamelCase_      =	x[0]
    for i in x:
        if abs(SCREAMING_SNAKE_CASE_ ) > abs(SCREAMING_SNAKE_CASE_ ):
            UpperCamelCase_      =	i
    return j




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    if len(SCREAMING_SNAKE_CASE_ ) == 0:
        raise ValueError("abs_max_sort() arg is an empty sequence" )
    return sorted(SCREAMING_SNAKE_CASE_ ,	key=SCREAMING_SNAKE_CASE_ )[-1]




def  lowerCAmelCase(			)->	Any:



    """simple docstring"""






    assert abs_val(0 ) == 0
    assert abs_val(3_4 ) == 3_4
    assert abs_val(-1_0_0_0_0_0_0_0_0_0_0_0 ) == 1_0_0_0_0_0_0_0_0_0_0_0

    UpperCamelCase_      =	[-3, -1, 2, -1_1]
    assert abs_max(SCREAMING_SNAKE_CASE_ ) == -1_1
    assert abs_max_sort(SCREAMING_SNAKE_CASE_ ) == -1_1
    assert abs_min(SCREAMING_SNAKE_CASE_ ) == -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 test_abs_val()
 print(abs_val(-34))  # --> 34
