



"""simple docstring"""




import copy
import re






class      UpperCamelCase    :
	lowerCAmelCase	:   List[str]				=	"""hp"""
	lowerCAmelCase	:   Dict				=	{}
	lowerCAmelCase	:   List[str]				=	None



	@classmethod
	def 			__A							(       cls			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       prefix
							A__							=       defaults
							cls.build_naming_info()



	@staticmethod
	def 			__A							(       UpperCAmelCase__			,	UpperCAmelCase__		):
							if len(__SCREAMING_SNAKE_CASE		) == 0:
													return ""
							A__							=       None
							if any(char.isdigit() for char in word		):
													raise Exception(F"""Parameters should not contain numbers: '{word}' contains a number"""		)
							if word in info["short_word"]:
													return info["short_word"][word]
							for prefix_len in range(1			,	len(__SCREAMING_SNAKE_CASE		) + 1		):
													A__							=       word[:prefix_len]
													if prefix in info["reverse_short_word"]:
																			continue
													else:
																			A__							=       prefix
																			break

							if short_word is None:
													# Paranoid fallback
													def int_to_alphabetic(UpperCAmelCase__		):
																			A__							=       ""
																			while integer != 0:
																									A__							=       chr(ord("A"		) + integer % 10		) + s
																									integer //= 10
																			return s

													A__							=       0
													while True:
																			A__							=       word + "#" + int_to_alphabetic(__SCREAMING_SNAKE_CASE		)
																			if sword in info["reverse_short_word"]:
																									continue
																			else:
																									A__							=       sword
																									break

							A__							=       short_word
							A__							=       word
							return short_word



	@staticmethod
	def 			__A							(       UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       param_name.split("_"		)

							A__							=       [TrialShortNamer.shortname_for_word(__SCREAMING_SNAKE_CASE			,	__SCREAMING_SNAKE_CASE		) for word in words]

							# We try to create a separatorless short name, but if there is a collision we have to fallback
							# to a separated short name
							A__							=       ["", "_"]

							for separator in separators:
													A__							=       separator.join(__SCREAMING_SNAKE_CASE		)
													if shortname not in info["reverse_short_param"]:
																			A__							=       shortname
																			A__							=       param_name
																			return shortname

							return param_name



	@staticmethod
	def 			__A							(       UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       TrialShortNamer.shortname_for_key(__SCREAMING_SNAKE_CASE			,	__SCREAMING_SNAKE_CASE		)
							A__							=       short_name
							A__							=       param_name



	@classmethod
	def 			__A							(       cls		):
							if cls.NAMING_INFO is not None:
													return

							A__							=       {
							    "short_word": {},
							    "reverse_short_word": {},
							    "short_param": {},
							    "reverse_short_param": {},
							}

							A__							=       list(cls.DEFAULTS.keys()		)

							for k in field_keys:
													cls.add_new_param_name(__SCREAMING_SNAKE_CASE			,	__SCREAMING_SNAKE_CASE		)

							A__							=       info



	@classmethod
	def 			__A							(       cls			,	UpperCAmelCase__		):
							cls.build_naming_info()
							assert cls.PREFIX is not None
							A__							=       [copy.copy(cls.PREFIX		)]

							for k, v in params.items():
													if k not in cls.DEFAULTS:
																			raise Exception(F"""You should provide a default value for the param name {k} with value {v}"""		)
													if v == cls.DEFAULTS[k]:
																			# The default value is not added to the name
																			continue

													A__							=       cls.NAMING_INFO["short_param"][k]

													if isinstance(__SCREAMING_SNAKE_CASE			,	__SCREAMING_SNAKE_CASE		):
																			A__							=       1 if v else 0

													A__							=       "" if isinstance(__SCREAMING_SNAKE_CASE			,	(int, float)		) else "-"
													A__							=       F"""{key}{sep}{v}"""
													name.append(__SCREAMING_SNAKE_CASE		)

							return "_".join(__SCREAMING_SNAKE_CASE		)



	@classmethod
	def 			__A							(       cls			,	UpperCAmelCase__		):
							A__							=       repr[len(cls.PREFIX		) + 1 :]
							if repr == "":
													A__							=       []
							else:
													A__							=       repr.split("_"		)

							A__							=       {}

							for value in values:
													if "-" in value:
																			A__						,       A__							=       value.split("-"		)
													else:
																			A__							=       re.sub("[0-9.]"			,	""			,	__SCREAMING_SNAKE_CASE		)
																			A__							=       float(re.sub("[^0-9.]"			,	""			,	__SCREAMING_SNAKE_CASE		)		)

													A__							=       cls.NAMING_INFO["reverse_short_param"][p_k]

													A__							=       p_v

							for k in cls.DEFAULTS:
													if k not in parameters:
																			A__							=       cls.DEFAULTS[k]

							return parameters










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  UpperCamelCase						(       _A			:				Tuple    )->       Dict:





						"""simple docstring"""





						A__							=       [
						    "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  UpperCamelCase						(       _A			:				int    )->       Optional[Any]:





						"""simple docstring"""





						A__						,       A__							=       emb.weight.shape
						A__							=       nn.Linear(_A			,							_A			,							bias=_A    )
						A__							=       emb.weight.data
						return lin_layer
def  UpperCamelCase						(       _A			:				str			,							_A			:				Optional[Any]=None    )->       str:





						"""simple docstring"""





						A__							=       {}
						for old_key in state_dict.keys():
												A__							=       old_key
												if "moe_layer.experts." in key:
																		if expert_idx is not None:
																								A__							=       key.replace("moe_layer.experts.0"			,							f"""ffn.experts.expert_{expert_idx}"""    )
																		else:
																								A__							=       key.replace("moe_layer.experts."			,							"ffn.experts.expert_"    )
												if "gate" in key:
																		A__							=       key.replace(".moe_layer.gate.wg"			,							".ffn.router.classifier"    )
												if "fc2" and "experts" not in key:
																		A__							=       key.replace(".fc2."			,							".ffn.fc2."    )
												if "fc1" and "experts" not in key:
																		A__							=       key.replace(".fc1."			,							".ffn.fc1."    )
												if ".encoder_attn." in key:
																		A__							=       key.replace(".encoder_attn."			,							".cross_attention."    )
												if "encoder_attn_layer_norm" in key:
																		A__							=       key.replace("encoder_attn_layer_norm"			,							"cross_attention_layer_norm"    )
												if "final_layer_norm" in key:
																		A__							=       key.replace("final_layer_norm"			,							"ff_layer_norm"    )
												A__							=       state_dict[old_key]
						return new_dict
def  UpperCamelCase						(       _A			:				Tuple			,							_A			:				Tuple			,							_A			:				int			,							_A			:				str			,							_A			:				str = WEIGHTS_NAME    )->       List[str]:





						"""simple docstring"""





						A__							=       []
						A__							=       0
						os.makedirs(_A			,							exist_ok=_A    )

						for expert in range(_A    ):
												A__							=       switch_checkpoint_path + f"""-rank-{expert}.pt"""
												if os.path.isfile(_A    ):
																		A__							=       torch.load(_A    )["model"]
																		remove_ignore_keys_(_A    )
																		A__							=       rename_fairseq_keys(_A			,							_A    )
																		A__							=       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
						A__							=       os.path.join(_A			,							weights_name.replace(".bin"			,							f"""-{len(_A    )+1:05d}-of-???.bin"""    )    )
						A__							=       torch.load(switch_checkpoint_path + "-shared.pt"    )["model"]
						remove_ignore_keys_(_A    )
						A__							=       rename_fairseq_keys(_A			,							_A    )
						A__							=       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:
												A__							=       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
						A__							=       {}
						for idx, shard in enumerate(_A    ):
												A__							=       weights_name.replace(".bin"			,							f"""-{idx+1:05d}-of-{len(_A    ):05d}.bin"""    )
												A__							=       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:
																		A__							=       shard_file

    # Add the metadata
						A__							=       {"total_size": total_size}
						A__							=       {"metadata": metadata, "weight_map": weight_map}

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

						return metadata, index


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  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_							:      Union[str, Any]      =  parser.parse_args()
						UpperCAmelCase_   ,	UpperCAmelCase_							:      Optional[int]      =  shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						UpperCAmelCase_							:      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)
						UpperCAmelCase_							:      Tuple      =  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("Done")
						model.save_pretrained(args.pytorch_dump_folder_path)





