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

						snake_case							:  Any			=     restore_checkpoint(A_     ,				A_							)
						snake_case							:  Optional[Any]			=     checkpoint_dict['''target''']

						# Embeddings
						# Position IDs
						snake_case							:  Optional[int]			=     fnet_pretraining_model.state_dict()

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

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

												snake_case							:  List[Any]			=     torch.tensor(
												    pretrained_model_params['''encoder'''][F"""feed_forward_{layer}"""]['''intermediate''']['''kernel''']							).T
												snake_case							:  str			=     torch.tensor(
												    pretrained_model_params['''encoder'''][F"""feed_forward_{layer}"""]['''intermediate''']['''bias''']							)

												snake_case							:  Optional[int]			=     torch.tensor(
												    pretrained_model_params['''encoder'''][F"""feed_forward_{layer}"""]['''output''']['''kernel''']							).T
												snake_case							:  Tuple			=     torch.tensor(
												    pretrained_model_params['''encoder'''][F"""feed_forward_{layer}"""]['''output''']['''bias''']							)

												snake_case							:  Optional[Any]			=     torch.tensor(
												    pretrained_model_params['''encoder'''][F"""encoder_{layer}"""]['''output_layer_norm''']['''scale''']							)
												snake_case							:  Any			=     torch.tensor(
												    pretrained_model_params['''encoder'''][F"""encoder_{layer}"""]['''output_layer_norm''']['''bias''']							)

						# Pooler Layers
						snake_case							:  int			=     torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']							).T
						snake_case							:  Union[str, Any]			=     torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']							)

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

						# Seq Relationship Layers
						snake_case							:  int			=     torch.tensor(
						    pretrained_model_params['''classification''']['''output_kernel''']							)
						snake_case							:  Any			=     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            = 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)






from __future__ import annotations

import sys







class 				a_    :

						def __init__(       self      :Dict  ,		_lowercase      :str  ,		_lowercase      :int)						->   List[Any]:
												UpperCAmelCase_					=	letter
												UpperCAmelCase_					=	freq
												UpperCAmelCase_					=	{}




						def __repr__(       self      :str)						->   str:
												return f"{self.letter}:{self.freq}"







class 				a_    :

						def __init__(       self      :List[Any]  ,		_lowercase      :int  ,		_lowercase      :Letter | TreeNode  ,		_lowercase      :Letter | TreeNode)						->   Dict:
												UpperCAmelCase_					=	freq
												UpperCAmelCase_					=	left
												UpperCAmelCase_					=	right







def 							A     (						__UpperCAmelCase      )			->			list[Letter]:



						'''simple docstring'''






						UpperCAmelCase_					=	{}
						with open(__UpperCAmelCase      ) as f:
												while True:
																		UpperCAmelCase_					=	f.read(1      )
																		if not c:
																								break
																		UpperCAmelCase_					=	chars[c] + 1 if c in chars else 1
						return sorted((Letter(__UpperCAmelCase				,   __UpperCAmelCase      ) for c, f in chars.items())				,   key=lambda __UpperCAmelCase      : x.freq      )







def 							A     (						__UpperCAmelCase      )			->			Letter | TreeNode:



						'''simple docstring'''






						UpperCAmelCase_					=	letters  # type: ignore
						while len(__UpperCAmelCase      ) > 1:
												UpperCAmelCase_					=	response.pop(0      )
												UpperCAmelCase_					=	response.pop(0      )
												UpperCAmelCase_					=	left.freq + right.freq
												UpperCAmelCase_					=	TreeNode(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )
												response.append(__UpperCAmelCase      )
												response.sort(key=lambda __UpperCAmelCase      : x.freq      )
						return response[0]







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			list[Letter]:



						'''simple docstring'''






						if isinstance(__UpperCAmelCase				,   __UpperCAmelCase      ):
												UpperCAmelCase_					=	bitstring
												return [root]
						UpperCAmelCase_					=	root  # type: ignore
						UpperCAmelCase_					=	[]
						letters += traverse_tree(treenode.left				,   bitstring + '''0'''      )
						letters += traverse_tree(treenode.right				,   bitstring + '''1'''      )
						return letters







def 							A     (						__UpperCAmelCase      )			->			None:



						'''simple docstring'''






						UpperCAmelCase_					=	parse_file(__UpperCAmelCase      )
						UpperCAmelCase_					=	build_tree(__UpperCAmelCase      )
						UpperCAmelCase_					=	{
						    k: v for letter in traverse_tree(__UpperCAmelCase				,   ''''''      ) for k, v in letter.bitstring.items()
						}
						print(f"Huffman Coding  of {file_path}: "      )
						with open(__UpperCAmelCase      ) as f:
												while True:
																		UpperCAmelCase_					=	f.read(1      )
																		if not c:
																								break
																		print(letters[c]				,   end=''' '''      )
						print()


if __name__ == "__main__":
							# pass the file path to the huffman function
							huffman(sys.argv[1])





