
"""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 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			[
		    '''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 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			emb.weight.shape
		SCREAMING_SNAKE_CASE_    			=			nn.Linear(__a,				__a,				bias=__a     )
		SCREAMING_SNAKE_CASE_    			=			emb.weight.data
		return lin_layer



def 							_lowerCamelCase			(			__a,				__a=None     ):
		SCREAMING_SNAKE_CASE_    			=			{}
		for old_key in state_dict.keys():
				SCREAMING_SNAKE_CASE_    			=			old_key
				if "moe_layer.experts." in key:
						if expert_idx is not None:
								SCREAMING_SNAKE_CASE_    			=			key.replace('''moe_layer.experts.0''',				F'ffn.experts.expert_{expert_idx}'     )
						else:
								SCREAMING_SNAKE_CASE_    			=			key.replace('''moe_layer.experts.''',				'''ffn.experts.expert_'''     )
				if "gate" in key:
						SCREAMING_SNAKE_CASE_    			=			key.replace('''.moe_layer.gate.wg''',				'''.ffn.router.classifier'''     )
				if "fc2" and "experts" not in key:
						SCREAMING_SNAKE_CASE_    			=			key.replace('''.fc2.''',				'''.ffn.fc2.'''     )
				if "fc1" and "experts" not in key:
						SCREAMING_SNAKE_CASE_    			=			key.replace('''.fc1.''',				'''.ffn.fc1.'''     )
				if ".encoder_attn." in key:
						SCREAMING_SNAKE_CASE_    			=			key.replace('''.encoder_attn.''',				'''.cross_attention.'''     )
				if "encoder_attn_layer_norm" in key:
						SCREAMING_SNAKE_CASE_    			=			key.replace('''encoder_attn_layer_norm''',				'''cross_attention_layer_norm'''     )
				if "final_layer_norm" in key:
						SCREAMING_SNAKE_CASE_    			=			key.replace('''final_layer_norm''',				'''ff_layer_norm'''     )
				SCREAMING_SNAKE_CASE_    			=			state_dict[old_key]
		return new_dict



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a = WEIGHTS_NAME     ):
		SCREAMING_SNAKE_CASE_    			=			[]
		SCREAMING_SNAKE_CASE_    			=			0
		os.makedirs(__a,				exist_ok=__a     )

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

    # Add the metadata
		SCREAMING_SNAKE_CASE_    			=			{'''total_size''': total_size}
		SCREAMING_SNAKE_CASE_    			=			{'''metadata''': metadata, '''weight_map''': weight_map}

		with open(os.path.join(__a,				__a     ),				'''w''',				encoding='''utf-8'''     ) as f:
				SCREAMING_SNAKE_CASE_    			=			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,
						    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)

"""simple docstring"""




import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('>=', FSDP_PYTORCH_VERSION):
						import torch.distributed.checkpoint as dist_cp
						from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
						from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
						from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
						from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


lowerCAmelCase__				=					get_logger(__name__)



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a=0     ):
		os.makedirs(__a,				exist_ok=__a     )
		with FSDP.state_dict_type(
		    __a,				fsdp_plugin.state_dict_type,				fsdp_plugin.state_dict_config,				fsdp_plugin.optim_state_dict_config     ):
				SCREAMING_SNAKE_CASE_    			=			model.state_dict()
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						SCREAMING_SNAKE_CASE_    			=			F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )
						if accelerator.process_index == 0:
								logger.info(F'Saving model to {output_model_file}'     )
								torch.save(__a,				__a     )
								logger.info(F'Model saved to {output_model_file}'     )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						SCREAMING_SNAKE_CASE_    			=			(
						    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
						    if model_index == 0
						    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
						)
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )
						logger.info(F'Saving model to {output_model_file}'     )
						torch.save(__a,				__a     )
						logger.info(F'Model saved to {output_model_file}'     )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				F'{MODEL_NAME}_{model_index}'     )
						os.makedirs(__a,				exist_ok=__a     )
						logger.info(F'Saving model to {ckpt_dir}'     )
						SCREAMING_SNAKE_CASE_    			=			{'''model''': state_dict}

						dist_cp.save_state_dict(
						    state_dict=__a,				storage_writer=dist_cp.FileSystemWriter(__a     ),				planner=DefaultSavePlanner(),				)
						logger.info(F'Model saved to {ckpt_dir}'     )



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a=0     ):
		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    __a,				fsdp_plugin.state_dict_type,				fsdp_plugin.state_dict_config,				fsdp_plugin.optim_state_dict_config     ):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if type(__a     ) != FSDP and accelerator.process_index != 0:
								if not fsdp_plugin.sync_module_states:
										raise ValueError(
										    '''Set the `sync_module_states` flag to `True` so that model states are synced across processes when '''
										    '''initializing FSDP object'''     )
								return
						SCREAMING_SNAKE_CASE_    			=			F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )
						logger.info(F'Loading model from {input_model_file}'     )
						SCREAMING_SNAKE_CASE_    			=			torch.load(__a     )
						logger.info(F'Model loaded from {input_model_file}'     )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						SCREAMING_SNAKE_CASE_    			=			(
						    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
						    if model_index == 0
						    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
						)
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )
						logger.info(F'Loading model from {input_model_file}'     )
						SCREAMING_SNAKE_CASE_    			=			torch.load(__a     )
						logger.info(F'Model loaded from {input_model_file}'     )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						SCREAMING_SNAKE_CASE_    			=			(
						    os.path.join(__a,				F'{MODEL_NAME}_{model_index}'     )
						    if F'{MODEL_NAME}' not in input_dir
						    else input_dir
						)
						logger.info(F'Loading model from {ckpt_dir}'     )
						SCREAMING_SNAKE_CASE_    			=			{'''model''': model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=__a,				storage_reader=dist_cp.FileSystemReader(__a     ),				planner=DefaultLoadPlanner(),				)
						SCREAMING_SNAKE_CASE_    			=			state_dict['''model''']
						logger.info(F'Model loaded from {ckpt_dir}'     )
				model.load_state_dict(__a     )



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a,				__a=0     ):
		os.makedirs(__a,				exist_ok=__a     )
		with FSDP.state_dict_type(
		    __a,				fsdp_plugin.state_dict_type,				fsdp_plugin.state_dict_config,				fsdp_plugin.optim_state_dict_config     ):
				SCREAMING_SNAKE_CASE_    			=			FSDP.optim_state_dict(__a,				__a     )
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								SCREAMING_SNAKE_CASE_    			=			(
								    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
								)
								SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )
								logger.info(F'Saving Optimizer state to {output_optimizer_file}'     )
								torch.save(__a,				__a     )
								logger.info(F'Optimizer state saved in {output_optimizer_file}'     )
				else:
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				F'{OPTIMIZER_NAME}_{optimizer_index}'     )
						os.makedirs(__a,				exist_ok=__a     )
						logger.info(F'Saving Optimizer state to {ckpt_dir}'     )
						dist_cp.save_state_dict(
						    state_dict={'''optimizer''': optim_state},				storage_writer=dist_cp.FileSystemWriter(__a     ),				planner=DefaultSavePlanner(),				)
						logger.info(F'Optimizer state saved in {ckpt_dir}'     )



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a,				__a=0     ):
		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    __a,				fsdp_plugin.state_dict_type,				fsdp_plugin.state_dict_config,				fsdp_plugin.optim_state_dict_config     ):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						SCREAMING_SNAKE_CASE_    			=			None
						# below check should work but currently it isn't working (mostly opytorch issue),
						# in the meantime disabling it at the cost of excess memory usage
						# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
						SCREAMING_SNAKE_CASE_    			=			(
						    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
						)
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )
						logger.info(F'Loading Optimizer state from {input_optimizer_file}'     )
						SCREAMING_SNAKE_CASE_    			=			torch.load(__a     )
						logger.info(F'Optimizer state loaded from {input_optimizer_file}'     )
				else:
						SCREAMING_SNAKE_CASE_    			=			(
						    os.path.join(__a,				F'{OPTIMIZER_NAME}_{optimizer_index}'     )
						    if F'{OPTIMIZER_NAME}' not in input_dir
						    else input_dir
						)
						logger.info(F'Loading Optimizer from {ckpt_dir}'     )
						SCREAMING_SNAKE_CASE_    			=			load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict(),				optimizer_key='''optimizer''',				storage_reader=dist_cp.FileSystemReader(__a     ),				)
						SCREAMING_SNAKE_CASE_    			=			optim_state['''optimizer''']
						logger.info(F'Optimizer loaded from {ckpt_dir}'     )
				SCREAMING_SNAKE_CASE_    			=			FSDP.optim_state_dict_to_load(__a,				__a,				__a     )
				optimizer.load_state_dict(__a     )