





'''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      __A						(							a_						:  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(a_      ,a_	)






def      __A						(							a_						:  int	):
		lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   emb.weight.shape
		lowerCAmelCase     :    Dict						=   nn.Linear(a_      ,a_      ,bias=a_	)
		lowerCAmelCase     :    Optional[Any]						=   emb.weight.data
		return lin_layer






def      __A						(							a_						:  List[Any]      ,a_						:  Optional[Any]=None	):
		lowerCAmelCase     :    Any						=   {}
		for old_key in state_dict.keys():
				lowerCAmelCase     :    Dict						=   old_key
				if "moe_layer.experts." in key:
						if expert_idx is not None:
								lowerCAmelCase     :    Union[str, Any]						=   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     :    str						=   key.replace(".moe_layer.gate.wg"      ,".ffn.router.classifier"	)
				if "fc2" and "experts" not in key:
						lowerCAmelCase     :    List[Any]						=   key.replace(".fc2."      ,".ffn.fc2."	)
				if "fc1" and "experts" not in key:
						lowerCAmelCase     :    Dict						=   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     :    Tuple						=   key.replace("final_layer_norm"      ,"ff_layer_norm"	)
				lowerCAmelCase     :    Optional[Any]						=   state_dict[old_key]
		return new_dict






def      __A						(							a_						:  Any      ,a_						:  List[Any]      ,a_						:  Any      ,a_						:  Optional[int]      ,a_						:  str = WEIGHTS_NAME	):
		lowerCAmelCase     :    Optional[Any]						=   []
		lowerCAmelCase     :    List[str]						=   0
		os.makedirs(a_      ,exist_ok=a_	)

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

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

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

		with open(os.path.join(a_      ,a_	)      ,"w"      ,encoding="utf-8"	) as f:
				lowerCAmelCase     :    List[str]						=   json.dumps(a_      ,indent=2      ,sort_keys=a_	) + "\n"
				f.write(a_	)

		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,
					    1_28,
					    args.dtype,
					)

					lowerCAmelCase       		=      NllbMoeConfig.from_pretrained(
					    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
					)
					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)










'''simple docstring'''






import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def      __A						(							a_						:  List[str]      ,a_						:  Dict      ,a_						:  List[Any]      ,a_						:  Optional[int]=1_0_2_4	):
		lowerCAmelCase	,		lowerCAmelCase     :    Dict						=   [], []

		lowerCAmelCase     :    Optional[Any]						=   list(zip(a_      ,a_	)	)
		lowerCAmelCase	,		lowerCAmelCase     :    Any						=   sorted_examples[0]

		def is_too_big(a_						:  Any	):
				return tok(a_      ,return_tensors="pt"	).input_ids.shape[1] > max_tokens

		for src, tgt in tqdm(sorted_examples[1:]	):
				lowerCAmelCase     :    Optional[int]						=   new_src + " " + src
				lowerCAmelCase     :    Union[str, Any]						=   new_tgt + " " + tgt
				if is_too_big(a_	) or is_too_big(a_	):  # cant fit, finalize example
						finished_src.append(a_	)
						finished_tgt.append(a_	)
						lowerCAmelCase	,		lowerCAmelCase     :    str						=   src, tgt
				else:  # can fit, keep adding
						lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   cand_src, cand_tgt

    # cleanup
		if new_src:
				assert new_tgt
				finished_src.append(a_	)
				finished_tgt.append(a_	)
		return finished_src, finished_tgt






def      __A						(							a_						:  List[str]      ,a_						:  Path      ,a_						:  Any      ,a_						:  int	):
		lowerCAmelCase     :    Optional[int]						=   Path(a_	)
		save_path.mkdir(exist_ok=a_	)
		for split in ["train"]:
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
				lowerCAmelCase     :    Tuple						=   [x.rstrip() for x in Path(a_	).open().readlines()]
				lowerCAmelCase     :    str						=   [x.rstrip() for x in Path(a_	).open().readlines()]
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   pack_examples(a_      ,a_      ,a_      ,a_	)
				print(f'''packed {split} split from {len(a_	)} examples -> {len(a_	)}.'''	)
				Path(save_path / f'''{split}.source'''	).open("w"	).write("\n".join(a_	)	)
				Path(save_path / f'''{split}.target'''	).open("w"	).write("\n".join(a_	)	)
		for split in ["val", "test"]:
				lowerCAmelCase	,		lowerCAmelCase     :    Optional[Any]						=   data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
				shutil.copyfile(a_      ,save_path / f'''{split}.source'''	)
				shutil.copyfile(a_      ,save_path / f'''{split}.target'''	)






def      __A						(							):
		lowerCAmelCase     :    List[str]						=   argparse.ArgumentParser()
		parser.add_argument("--tok_name"      ,type=a_      ,help="like facebook/bart-large-cnn,t5-base, etc."	)
		parser.add_argument("--max_seq_len"      ,type=a_      ,default=1_2_8	)
		parser.add_argument("--data_dir"      ,type=a_	)
		parser.add_argument("--save_path"      ,type=a_	)
		lowerCAmelCase     :    Optional[Any]						=   parser.parse_args()
		lowerCAmelCase     :    List[str]						=   AutoTokenizer.from_pretrained(args.tok_name	)
		return pack_data_dir(a_      ,Path(args.data_dir	)      ,args.max_seq_len      ,args.save_path	)


if __name__ == "__main__":
					packer_cli()



