

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_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, 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(_UpperCAmelCase				,	_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       emb.weight.shape
						SCREAMING_SNAKE_CASE_: Dict  		=       nn.Linear(_UpperCAmelCase				,	_UpperCAmelCase				,	bias=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: List[str]  		=       emb.weight.data
						return lin_layer







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







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase = WEIGHTS_NAME    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       []
						SCREAMING_SNAKE_CASE_: Any  		=       0
						os.makedirs(_UpperCAmelCase				,	exist_ok=_UpperCAmelCase    )

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

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

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

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

						return metadata, index


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

						lowerCAmelCase							:  Union[str, 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)
						lowerCAmelCase							:  int   	=						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("""Done""")
						model.save_pretrained(args.pytorch_dump_folder_path)









from collections.abc import Callable



class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     Tuple   ,						lowerCAmelCase__  :     Callable | None = None):
									# Stores actual heap items.
									SCREAMING_SNAKE_CASE_: list  		=       []
									# Stores indexes of each item for supporting updates and deletion.
									SCREAMING_SNAKE_CASE_: dict  		=       {}
									# Stores current size of heap.
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       0
									# Stores function used to evaluate the score of an item on which basis ordering
									# will be done.
									SCREAMING_SNAKE_CASE_: Any  		=       key or (lambda lowerCAmelCase__: x)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     int):
									return int((i - 1) / 2) if i > 0 else None



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       int(2 * i + 1)
									return left if 0 < left < self.size else None



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       int(2 * i + 2)
									return right if 0 < right < self.size else None



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       (
									    self.pos_map[self.arr[j][0]],
									    self.pos_map[self.arr[i][0]],
									)
									# Then swap the items in the list.
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       self.arr[j], self.arr[i]



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									return self.arr[i][1] < self.arr[j][1]



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Any  		=       self._left(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._right(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       i

									if left is not None and not self._cmp(lowerCAmelCase__   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       left
									if right is not None and not self._cmp(lowerCAmelCase__   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Tuple  		=       right

									return valid_parent



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._parent(lowerCAmelCase__)
									while parent is not None and not self._cmp(lowerCAmelCase__   ,						lowerCAmelCase__):
															self._swap(lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       parent, self._parent(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self._get_valid_parent(lowerCAmelCase__)
									while valid_parent != index:
															self._swap(lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       valid_parent, self._get_valid_parent(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									if item not in self.pos_map:
															return
									SCREAMING_SNAKE_CASE_: Any  		=       self.pos_map[item]
									SCREAMING_SNAKE_CASE_: int  		=       [item, self.key(lowerCAmelCase__)]
									# Make sure heap is right in both up and down direction.
									# Ideally only one of them will make any change.
									self._heapify_up(lowerCAmelCase__)
									self._heapify_down(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     int):
									if item not in self.pos_map:
															return
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.pos_map[item]
									del self.pos_map[item]
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.arr[self.size - 1]
									SCREAMING_SNAKE_CASE_: Tuple  		=       index
									self.size -= 1
									# Make sure heap is right in both up and down direction. Ideally only one
									# of them will make any change- so no performance loss in calling both.
									if self.size > index:
															self._heapify_up(lowerCAmelCase__)
															self._heapify_down(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       len(self.arr)
									if arr_len == self.size:
															self.arr.append([item, self.key(lowerCAmelCase__)])
									else:
															SCREAMING_SNAKE_CASE_: str  		=       [item, self.key(lowerCAmelCase__)]
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.size
									self.size += 1
									self._heapify_up(self.size - 1)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return self.arr[0] if self.size else None




			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_top()
									if top_item_tuple:
															self.delete_item(top_item_tuple[0])
									return top_item_tuple







def 			A_      (    ):
						pass


if __name__ == "__main__":
						import doctest

						doctest.testmod()






