

'''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       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]     ,							_SCREAMING_SNAKE_CASE					:    Dict     ,							_SCREAMING_SNAKE_CASE					:    List[Any]							)			-> Tuple:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[int]      =   FNetConfig.from_json_file(_SCREAMING_SNAKE_CASE							)
	print(F'''Building PyTorch model from configuration: {config}'''							)
	UpperCAmelCase_			:	Optional[Any]      =   FNetForPreTraining(_SCREAMING_SNAKE_CASE							)

	UpperCAmelCase_			:	List[str]      =   restore_checkpoint(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	int      =   checkpoint_dict["target"]

	# Embeddings
	# Position IDs
	UpperCAmelCase_			:	Optional[Any]      =   fnet_pretraining_model.state_dict()

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

	# Encoder Layers
	for layer in range(config.num_hidden_layers							):
		UpperCAmelCase_			:	int      =   torch.tensor(
		    pretrained_model_params["encoder"][F'''encoder_{layer}''']["mixing_layer_norm"]["scale"]							)
		UpperCAmelCase_			:	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_			:	List[str]      =   torch.tensor(
		    pretrained_model_params["encoder"][F'''feed_forward_{layer}''']["intermediate"]["bias"]							)

		UpperCAmelCase_			:	str      =   torch.tensor(
		    pretrained_model_params["encoder"][F'''feed_forward_{layer}''']["output"]["kernel"]							).T
		UpperCAmelCase_			:	List[str]      =   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[Any]      =   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_			:	Union[str, Any]      =   torch.tensor(
	    pretrained_model_params["predictions_dense"]["bias"]							)
	UpperCAmelCase_			:	Tuple      =   torch.tensor(
	    pretrained_model_params["predictions_layer_norm"]["scale"]							)
	UpperCAmelCase_			:	Union[str, Any]      =   torch.tensor(
	    pretrained_model_params["predictions_layer_norm"]["bias"]							)
	UpperCAmelCase_			:	List[Any]      =   torch.tensor(
	    pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]							)
	UpperCAmelCase_			:	Optional[int]      =   torch.tensor(
	    pretrained_model_params["predictions_output"]["output_bias"]							)
	UpperCAmelCase_			:	Optional[Any]      =   torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]							)

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







'''simple docstring'''
from __future__ import annotations

import re


def       a__    (  _SCREAMING_SNAKE_CASE					:    list[str]							)			-> list[str]:


	"""simple docstring"""





	def alphanum_key(_SCREAMING_SNAKE_CASE					:    Optional[Any]							):
		return [int(_SCREAMING_SNAKE_CASE							) if s.isdigit() else s.lower() for s in re.split("([0-9]+)"     ,							_SCREAMING_SNAKE_CASE							)]

	return sorted(_SCREAMING_SNAKE_CASE     ,							key=_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




