





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_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  FNetConfig.from_json_file(A__ )
	print(f"""Building PyTorch model from configuration: {config}""" )
	UpperCAmelCase_		    =  FNetForPreTraining(A__ )

	UpperCAmelCase_		    =  restore_checkpoint(A__					,   A__ )
	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(A__ )

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


if __name__ == "__main__":
				UpperCamelCase__ :			Dict       	=			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__ :			Tuple       	=			parser.parse_args()
				convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)








from typing import Callable




def    lowerCamelCase__			(		A__			:   Callable      ):





       '''simple docstring'''



       def wrapper(A__			:   int      ):
              __lowerCamelCase  =		function_with_docstring.__doc__
              return target_function

       return wrapper

