






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 					lowercase_ (    __snake_case     :							Union[str, Any]      )     -> Dict:





				'''simple docstring'''


				snake_case__      :str         =   [
				    "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(__snake_case	,	__snake_case      )





def 					lowercase_ (    __snake_case     :							Optional[int]      )     -> str:





				'''simple docstring'''


				snake_case__    ,						snake_case__      :Optional[int]         =   emb.weight.shape
				snake_case__      :Any         =   nn.Linear(__snake_case	,	__snake_case	,	bias=__snake_case      )
				snake_case__      :Dict         =   emb.weight.data
				return lin_layer





def 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							Any=None      )     -> List[Any]:





				'''simple docstring'''


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





def 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							Optional[Any]	,	__snake_case     :							Tuple	,	__snake_case     :							List[Any]	,	__snake_case     :							str = WEIGHTS_NAME      )     -> List[Any]:





				'''simple docstring'''


				snake_case__      :Union[str, Any]         =   []
				snake_case__      :Union[str, Any]         =   0
				os.makedirs(__snake_case	,	exist_ok=__snake_case      )

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

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

    # Add the metadata
				snake_case__      :Tuple         =   {"total_size": total_size}
				snake_case__      :Optional[Any]         =   {"metadata": metadata, "weight_map": weight_map}

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

				return metadata, index


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

	__UpperCAmelCase							:   int							=  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)
	__UpperCAmelCase							:   List[Any]							=  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
	print("Done")
	model.save_pretrained(args.pytorch_dump_folder_path)







__UpperCAmelCase							:   int							=  {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
__UpperCAmelCase							:   List[str]							=  ["a", "b", "c", "d", "e"]





def 					lowercase_ (    __snake_case     :							Optional[Any]	,	__snake_case     :							List[Any]	,	__snake_case     :							Tuple      )     -> Optional[int]:





				'''simple docstring'''


				snake_case__      :List[Any]         =   start
				# add current to visited
				visited.append(__snake_case      )
				snake_case__      :List[str]         =   edges[current]
				for neighbor in neighbors:
								# if neighbor not in visited, visit
								if neighbor not in visited:
												snake_case__      :Any         =   topological_sort(__snake_case	,	__snake_case	,	__snake_case      )
    # if all neighbors visited add current to sort
				sort.append(__snake_case      )
				# if all vertices haven't been visited select a new one to visit
				if len(__snake_case      ) != len(__snake_case      ):
								for vertice in vertices:
												if vertice not in visited:
																snake_case__      :Any         =   topological_sort(__snake_case	,	__snake_case	,	__snake_case      )
    # return sort
				return sort


if __name__ == "__main__":
	__UpperCAmelCase							:   Tuple							=  topological_sort("a", [], [])
	print(sort)