



"""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  __snake_case							(	__A      :						List[Any]     )     ->       Dict:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							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  __snake_case							(	__A      :						List[str]     )     ->       Optional[int]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Dict							=     emb.weight.shape
				SCREAMING_SNAKE_CASE						:							str							=     nn.Linear(__A							,		__A							,		bias=__A     )
				SCREAMING_SNAKE_CASE						:							Dict							=     emb.weight.data
				return lin_layer
def  __snake_case							(	__A      :						Optional[int]							,		__A      :						List[str]=None     )     ->       List[Any]:






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






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Dict							=     []
				SCREAMING_SNAKE_CASE						:							List[Any]							=     0
				os.makedirs(__A							,		exist_ok=__A     )

				for expert in range(__A     ):
								SCREAMING_SNAKE_CASE						:							List[str]							=     switch_checkpoint_path + F"""-rank-{expert}.pt"""
								if os.path.isfile(__A     ):
												SCREAMING_SNAKE_CASE						:							Tuple							=     torch.load(__A     )['model']
												remove_ignore_keys_(__A     )
												SCREAMING_SNAKE_CASE						:							List[str]							=     rename_fairseq_keys(__A							,		__A     )
												SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     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						:							Union[str, Any]							=     os.path.join(__A							,		weights_name.replace('.bin'							,		F"""-{len(__A     )+1:05d}-of-???.bin"""     )     )
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     torch.load(switch_checkpoint_path + '-shared.pt'     )['model']
				remove_ignore_keys_(__A     )
				SCREAMING_SNAKE_CASE						:							Tuple							=     rename_fairseq_keys(__A							,		__A     )
				SCREAMING_SNAKE_CASE						:							List[str]							=     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						:							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
				SCREAMING_SNAKE_CASE						:							str							=     {}
				for idx, shard in enumerate(__A     ):
								SCREAMING_SNAKE_CASE						:							Any							=     weights_name.replace('.bin'							,		F"""-{idx+1:05d}-of-{len(__A     ):05d}.bin"""     )
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     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						:							Optional[int]							=     shard_file

    # Add the metadata
				SCREAMING_SNAKE_CASE						:							Tuple							=     {'total_size': total_size}
				SCREAMING_SNAKE_CASE						:							str							=     {'metadata': metadata, 'weight_map': weight_map}

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

				return metadata, index


if __name__ == "__main__":
							A_						:    Tuple				=      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.',
							)
							A_						:    int				=      parser.parse_args()
							A_		,  A_						:    List[str]				=      shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    128,
							    args.dtype,
							)

							A_						:    List[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)
							A_						:    Any				=      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print('Done')
							model.save_pretrained(args.pytorch_dump_folder_path)










"""simple docstring"""

from typing import TYPE_CHECKING

from ..utils import _LazyModule


A_						:    str				=      {
    'config': [
        'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
        'OnnxConfig',
        'OnnxConfigWithPast',
        'OnnxSeq2SeqConfigWithPast',
        'PatchingSpec',
    ],
    'convert': ['export', 'validate_model_outputs'],
    'features': ['FeaturesManager'],
    'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}


if TYPE_CHECKING:
							from .config import (
							    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
							    OnnxConfig,
							    OnnxConfigWithPast,
							    OnnxSeqaSeqConfigWithPast,
							    PatchingSpec,
							)
							from .convert import export, validate_model_outputs
							from .features import FeaturesManager
							from .utils import ParameterFormat, compute_serialized_parameters_size

else:
							import sys

							A_						:    Optional[Any]				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





