
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__			(  SCREAMING_SNAKE_CASE_     )				->		List[str]:
			lowerCAmelCase__      :      Optional[Any]         =		[
			    '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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Any:
			lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[int]         =		emb.weight.shape
			lowerCAmelCase__      :      Union[str, Any]         =		nn.Linear(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							bias=SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Tuple         =		emb.weight.data
			return lin_layer
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=None     )				->		Optional[Any]:
			lowerCAmelCase__      :      Tuple         =		{}
			for old_key in state_dict.keys():
						lowerCAmelCase__      :      Optional[Any]         =		old_key
						if "moe_layer.experts." in key:
									if expert_idx is not None:
												lowerCAmelCase__      :      str         =		key.replace('moe_layer.experts.0'       ,							F'''ffn.experts.expert_{expert_idx}'''     )
									else:
												lowerCAmelCase__      :      str         =		key.replace('moe_layer.experts.'       ,							'ffn.experts.expert_'     )
						if "gate" in key:
									lowerCAmelCase__      :      Optional[int]         =		key.replace('.moe_layer.gate.wg'       ,							'.ffn.router.classifier'     )
						if "fc2" and "experts" not in key:
									lowerCAmelCase__      :      str         =		key.replace('.fc2.'       ,							'.ffn.fc2.'     )
						if "fc1" and "experts" not in key:
									lowerCAmelCase__      :      Tuple         =		key.replace('.fc1.'       ,							'.ffn.fc1.'     )
						if ".encoder_attn." in key:
									lowerCAmelCase__      :      List[Any]         =		key.replace('.encoder_attn.'       ,							'.cross_attention.'     )
						if "encoder_attn_layer_norm" in key:
									lowerCAmelCase__      :      Any         =		key.replace('encoder_attn_layer_norm'       ,							'cross_attention_layer_norm'     )
						if "final_layer_norm" in key:
									lowerCAmelCase__      :      Union[str, Any]         =		key.replace('final_layer_norm'       ,							'ff_layer_norm'     )
						lowerCAmelCase__      :      List[str]         =		state_dict[old_key]
			return new_dict
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_ = WEIGHTS_NAME     )				->		Dict:
			lowerCAmelCase__      :      Dict         =		[]
			lowerCAmelCase__      :      Optional[Any]         =		0
			os.makedirs(SCREAMING_SNAKE_CASE_       ,							exist_ok=SCREAMING_SNAKE_CASE_     )

			for expert in range(SCREAMING_SNAKE_CASE_     ):
						lowerCAmelCase__      :      Tuple         =		switch_checkpoint_path + F'''-rank-{expert}.pt'''
						if os.path.isfile(SCREAMING_SNAKE_CASE_     ):
									lowerCAmelCase__      :      str         =		torch.load(SCREAMING_SNAKE_CASE_     )['model']
									remove_ignore_keys_(SCREAMING_SNAKE_CASE_     )
									lowerCAmelCase__      :      int         =		rename_fairseq_keys(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )
									lowerCAmelCase__      :      str         =		os.path.join(
									    SCREAMING_SNAKE_CASE_       ,							weights_name.replace('.bin'       ,							F'''-{len(SCREAMING_SNAKE_CASE_     )+1:05d}-of-???.bin'''     )     )
									torch.save(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )
									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(SCREAMING_SNAKE_CASE_     )[0]].dtype     )

    # Add the last block
			lowerCAmelCase__      :      Optional[int]         =		os.path.join(SCREAMING_SNAKE_CASE_       ,							weights_name.replace('.bin'       ,							F'''-{len(SCREAMING_SNAKE_CASE_     )+1:05d}-of-???.bin'''     )     )
			lowerCAmelCase__      :      Optional[Any]         =		torch.load(switch_checkpoint_path + '-shared.pt'     )['model']
			remove_ignore_keys_(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      int         =		rename_fairseq_keys(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      int         =		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(SCREAMING_SNAKE_CASE_     ) == 1:
						lowerCAmelCase__      :      Optional[Any]         =		os.path.join(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )
						torch.save(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )
						return {weights_name: sharded_state_dicts[0]}, None
			else:
						torch.save(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )
			# Otherwise, let's build the index
			lowerCAmelCase__      :      Any         =		{}
			for idx, shard in enumerate(SCREAMING_SNAKE_CASE_     ):
						lowerCAmelCase__      :      Any         =		weights_name.replace('.bin'       ,							F'''-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_     ):05d}.bin'''     )
						lowerCAmelCase__      :      Optional[Any]         =		os.path.join(SCREAMING_SNAKE_CASE_       ,							weights_name.replace('.bin'       ,							F'''-{idx+1:05d}-of-???.bin'''     )     )
						os.rename(SCREAMING_SNAKE_CASE_       ,							os.path.join(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )     )
						for key in shard:
									lowerCAmelCase__      :      int         =		shard_file

    # Add the metadata
			lowerCAmelCase__      :      Any         =		{'total_size': total_size}
			lowerCAmelCase__      :      str         =		{'metadata': metadata, 'weight_map': weight_map}

			with open(os.path.join(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )       ,							'w'       ,							encoding='utf-8'     ) as f:
						lowerCAmelCase__      :      List[Any]         =		json.dumps(SCREAMING_SNAKE_CASE_       ,							indent=2       ,							sort_keys=SCREAMING_SNAKE_CASE_     ) + '\n'
						f.write(SCREAMING_SNAKE_CASE_     )

			return metadata, index


if __name__ == "__main__":
					lowerCamelCase__          =		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.""",
					)
					lowerCamelCase__          =		parser.parse_args()
					lowerCamelCase__      ,					lowerCamelCase__          =		shard_on_the_fly(
					    args.nllb_moe_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    128,
					    args.dtype,
					)

					lowerCamelCase__          =		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)
					lowerCamelCase__          =		NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
					print("""Done""")
					model.save_pretrained(args.pytorch_dump_folder_path)

import os
from distutils.util import strtobool
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Optional[Any]:
			for e in env_keys:
						lowerCAmelCase__      :      Union[str, Any]         =		int(os.environ.get(SCREAMING_SNAKE_CASE_       ,							-1     )     )
						if val >= 0:
									return val
			return default
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=False     )				->		List[str]:
			lowerCAmelCase__      :      Optional[int]         =		os.environ.get(SCREAMING_SNAKE_CASE_       ,							str(SCREAMING_SNAKE_CASE_     )     )
			return strtobool(SCREAMING_SNAKE_CASE_     ) == 1  # As its name indicates `strtobool` actually returns an int...







def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_="no"     )				->		List[str]:
			lowerCAmelCase__      :      Optional[int]         =		os.environ.get(SCREAMING_SNAKE_CASE_       ,							str(SCREAMING_SNAKE_CASE_     )     )
			return value