


import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Optional[Any]:


						'''simple docstring'''






						__snake_case				=		[
						    "encoder.version",
						    "decoder.version",
						    "model.encoder.version",
						    "model.decoder.version",
						    "decoder.output_projection.weight",
						    "_float_tensor",
						    "encoder.embed_positions._float_tensor",
						    "decoder.embed_positions._float_tensor",
						]
						for k in ignore_keys:
												state_dict.pop(_lowerCAmelCase      ,	_lowerCAmelCase )





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Dict:


						'''simple docstring'''






						__snake_case		,					__snake_case				=		emb.weight.shape
						__snake_case				=		nn.Linear(_lowerCAmelCase      ,	_lowerCAmelCase      ,	bias=_lowerCAmelCase )
						__snake_case				=		emb.weight.data
						return lin_layer





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase=None )						->	Any:


						'''simple docstring'''






						__snake_case				=		{}
						for old_key in state_dict.keys():
												__snake_case				=		old_key
												if "moe_layer.experts." in key:
																		if expert_idx is not None:
																								__snake_case				=		key.replace("moe_layer.experts.0"      ,	F'''ffn.experts.expert_{expert_idx}''' )
																		else:
																								__snake_case				=		key.replace("moe_layer.experts."      ,	"ffn.experts.expert_" )
												if "gate" in key:
																		__snake_case				=		key.replace(".moe_layer.gate.wg"      ,	".ffn.router.classifier" )
												if "fc2" and "experts" not in key:
																		__snake_case				=		key.replace(".fc2."      ,	".ffn.fc2." )
												if "fc1" and "experts" not in key:
																		__snake_case				=		key.replace(".fc1."      ,	".ffn.fc1." )
												if ".encoder_attn." in key:
																		__snake_case				=		key.replace(".encoder_attn."      ,	".cross_attention." )
												if "encoder_attn_layer_norm" in key:
																		__snake_case				=		key.replace("encoder_attn_layer_norm"      ,	"cross_attention_layer_norm" )
												if "final_layer_norm" in key:
																		__snake_case				=		key.replace("final_layer_norm"      ,	"ff_layer_norm" )
												__snake_case				=		state_dict[old_key]
						return new_dict





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase = WEIGHTS_NAME )						->	Union[str, Any]:


						'''simple docstring'''






						__snake_case				=		[]
						__snake_case				=		0
						os.makedirs(_lowerCAmelCase      ,	exist_ok=_lowerCAmelCase )

						for expert in range(_lowerCAmelCase ):
												__snake_case				=		switch_checkpoint_path + F'''-rank-{expert}.pt'''
												if os.path.isfile(_lowerCAmelCase ):
																		__snake_case				=		torch.load(_lowerCAmelCase )["model"]
																		remove_ignore_keys_(_lowerCAmelCase )
																		__snake_case				=		rename_fairseq_keys(_lowerCAmelCase      ,	_lowerCAmelCase )
																		__snake_case				=		os.path.join(
																		    _lowerCAmelCase      ,	weights_name.replace(".bin"      ,	F'''-{len(_lowerCAmelCase )+1:05d}-of-???.bin''' ) )
																		torch.save(_lowerCAmelCase      ,	_lowerCAmelCase )
																		sharded_state_dicts.append(expert_state.keys() )
																		total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
																		    expert_state[list(_lowerCAmelCase )[0]].dtype )

    # Add the last block
						__snake_case				=		os.path.join(_lowerCAmelCase      ,	weights_name.replace(".bin"      ,	F'''-{len(_lowerCAmelCase )+1:05d}-of-???.bin''' ) )
						__snake_case				=		torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
						remove_ignore_keys_(_lowerCAmelCase )
						__snake_case				=		rename_fairseq_keys(_lowerCAmelCase      ,	_lowerCAmelCase )
						__snake_case				=		shared_weights["decoder.embed_tokens.weight"]
						sharded_state_dicts.append(shared_weights.keys() )

						# If we only have the shared weights (dummy model/experts saved on the same file)
						if len(_lowerCAmelCase ) == 1:
												__snake_case				=		os.path.join(_lowerCAmelCase      ,	_lowerCAmelCase )
												torch.save(_lowerCAmelCase      ,	_lowerCAmelCase )
												return {weights_name: sharded_state_dicts[0]}, None
						else:
												torch.save(_lowerCAmelCase      ,	_lowerCAmelCase )
						# Otherwise, let's build the index
						__snake_case				=		{}
						for idx, shard in enumerate(_lowerCAmelCase ):
												__snake_case				=		weights_name.replace(".bin"      ,	F'''-{idx+1:05d}-of-{len(_lowerCAmelCase ):05d}.bin''' )
												__snake_case				=		os.path.join(_lowerCAmelCase      ,	weights_name.replace(".bin"      ,	F'''-{idx+1:05d}-of-???.bin''' ) )
												os.rename(_lowerCAmelCase      ,	os.path.join(_lowerCAmelCase      ,	_lowerCAmelCase ) )
												for key in shard:
																		__snake_case				=		shard_file

    # Add the metadata
						__snake_case				=		{"total_size": total_size}
						__snake_case				=		{"metadata": metadata, "weight_map": weight_map}

						with open(os.path.join(_lowerCAmelCase      ,	_lowerCAmelCase )      ,	"w"      ,	encoding="utf-8" ) as f:
												__snake_case				=		json.dumps(_lowerCAmelCase      ,	indent=2      ,	sort_keys=_lowerCAmelCase ) + "\n"
												f.write(_lowerCAmelCase )

						return metadata, index


if __name__ == "__main__":
	A							:					Optional[Any]     						=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--nllb_moe_checkpoint_path',
	    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
	    type=str,
	    required=False,
	    help='Path to a directory containing a folder per layer. Follows the original Google format.',
	)
	parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
	    type=str,
	    required=False,
	    help='Path to the output pytorch model.',
	)
	A							:					Any     						=     parser.parse_args()
	A						,  A							:					Union[str, Any]     						=     shard_on_the_fly(
	    args.nllb_moe_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    128,
	    args.dtype,
	)

	A							:					Optional[Any]     						=     NllbMoeConfig.from_pretrained(
	    'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
	)
	config.save_pretrained(args.pytorch_dump_folder_path)
	A							:					int     						=     NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
	print('Done')
	model.save_pretrained(args.pytorch_dump_folder_path)









from __future__ import annotations

from collections.abc import Iterator






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	None:




											'''simple docstring'''

											__snake_case				=		value
											__snake_case				=		None
											__snake_case				=		None






class     UpperCamelCase:


					def __init__(						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Node						)			->	None:




											'''simple docstring'''

											__snake_case				=		tree


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Node | None						)			->	int:




											'''simple docstring'''

											if node is None:
																	return 0
											return node.value + (
											    self.depth_first_search(node.left						) + self.depth_first_search(node.right						)
											)






					def __iter__(						self	:		int						)			->	Iterator[int]:




											'''simple docstring'''

											yield self.depth_first_search(self.tree						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





