




"""simple docstring"""







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 						snake_case__       (					_lowerCamelCase				)			->List[Any]:






		"""simple docstring"""


		__lowercase				:		Dict												=				[
		    "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 						snake_case__       (					_lowerCamelCase				)			->Optional[Any]:






		"""simple docstring"""


		__lowercase						,__lowercase				:		Optional[int]												=				emb.weight.shape
		__lowercase				:		Optional[Any]												=				nn.Linear(_lowerCamelCase,  _lowerCamelCase,  bias=_lowerCamelCase				)
		__lowercase				:		List[Any]												=				emb.weight.data
		return lin_layer

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=None				)			->Optional[int]:






		"""simple docstring"""


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

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase = WEIGHTS_NAME				)			->List[str]:






		"""simple docstring"""


		__lowercase				:		Any												=				[]
		__lowercase				:		Any												=				0
		os.makedirs(_lowerCamelCase,  exist_ok=_lowerCamelCase				)

		for expert in range(_lowerCamelCase				):
				__lowercase				:		Any												=				switch_checkpoint_path + F'-rank-{expert}.pt'
				if os.path.isfile(_lowerCamelCase				):
						__lowercase				:		List[str]												=				torch.load(_lowerCamelCase				)["model"]
						remove_ignore_keys_(_lowerCamelCase				)
						__lowercase				:		int												=				rename_fairseq_keys(_lowerCamelCase,  _lowerCamelCase				)
						__lowercase				:		int												=				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
		__lowercase				:		Any												=				os.path.join(_lowerCamelCase,  weights_name.replace(".bin",  F'-{len(_lowerCamelCase				)+1:05d}-of-???.bin'				)				)
		__lowercase				:		int												=				torch.load(switch_checkpoint_path + "-shared.pt"				)["model"]
		remove_ignore_keys_(_lowerCamelCase				)
		__lowercase				:		List[Any]												=				rename_fairseq_keys(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		Dict												=				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:
				__lowercase				:		List[Any]												=				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
		__lowercase				:		Optional[int]												=				{}
		for idx, shard in enumerate(_lowerCamelCase				):
				__lowercase				:		Tuple												=				weights_name.replace(".bin",  F'-{idx+1:05d}-of-{len(_lowerCamelCase				):05d}.bin'				)
				__lowercase				:		Union[str, Any]												=				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:
						__lowercase				:		Optional[Any]												=				shard_file

    # Add the metadata
		__lowercase				:		str												=				{"total_size": total_size}
		__lowercase				:		List[Any]												=				{"metadata": metadata, "weight_map": weight_map}

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

		return metadata, index


if __name__ == "__main__":
						__A   :  Optional[int]					=      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   :  Optional[Any]					=      parser.parse_args()
						__A,    __A   :  List[str]					=      shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    1_2_8,
						    args.dtype,
						)

						__A   :  Dict					=      NllbMoeConfig.from_pretrained(
						    'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
						)
						config.save_pretrained(args.pytorch_dump_folder_path)
						__A   :  Union[str, Any]					=      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print('Done')
						model.save_pretrained(args.pytorch_dump_folder_path)






"""simple docstring"""







def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		if a < 0:
				raise ValueError("Input value must be a positive integer"				)
		elif isinstance(_lowerCamelCase,  _lowerCamelCase				):
				raise TypeError("Input value must be a 'int' type"				)
		return bin(_lowerCamelCase				).count("1"				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
