






'''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 __UpperCAmelCase   (					A					:			Tuple		)    ->   Dict:
			UpperCAmelCase_      :       Optional[int]          =       [
			    '''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 __UpperCAmelCase   (					A					:			str		)    ->   List[str]:
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[Any]          =       emb.weight.shape
			UpperCAmelCase_      :       Any          =       nn.Linear(UpperCamelCase__			,       UpperCamelCase__			,       bias=UpperCamelCase__		)
			UpperCAmelCase_      :       str          =       emb.weight.data
			return lin_layer
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			List[str]=None		)    ->   str:
			UpperCAmelCase_      :       Dict          =       {}
			for old_key in state_dict.keys():
						UpperCAmelCase_      :       Dict          =       old_key
						if "moe_layer.experts." in key:
									if expert_idx is not None:
												UpperCAmelCase_      :       List[Any]          =       key.replace('''moe_layer.experts.0'''			,       F"ffn.experts.expert_{expert_idx}"		)
									else:
												UpperCAmelCase_      :       Optional[Any]          =       key.replace('''moe_layer.experts.'''			,       '''ffn.experts.expert_'''		)
						if "gate" in key:
									UpperCAmelCase_      :       Any          =       key.replace('''.moe_layer.gate.wg'''			,       '''.ffn.router.classifier'''		)
						if "fc2" and "experts" not in key:
									UpperCAmelCase_      :       List[str]          =       key.replace('''.fc2.'''			,       '''.ffn.fc2.'''		)
						if "fc1" and "experts" not in key:
									UpperCAmelCase_      :       str          =       key.replace('''.fc1.'''			,       '''.ffn.fc1.'''		)
						if ".encoder_attn." in key:
									UpperCAmelCase_      :       str          =       key.replace('''.encoder_attn.'''			,       '''.cross_attention.'''		)
						if "encoder_attn_layer_norm" in key:
									UpperCAmelCase_      :       Optional[int]          =       key.replace('''encoder_attn_layer_norm'''			,       '''cross_attention_layer_norm'''		)
						if "final_layer_norm" in key:
									UpperCAmelCase_      :       Optional[Any]          =       key.replace('''final_layer_norm'''			,       '''ff_layer_norm'''		)
						UpperCAmelCase_      :       Any          =       state_dict[old_key]
			return new_dict
def __UpperCAmelCase   (					A					:			Any			,       A					:			Optional[Any]			,       A					:			List[Any]			,       A					:			Tuple			,       A					:			str = WEIGHTS_NAME		)    ->   List[str]:
			UpperCAmelCase_      :       Dict          =       []
			UpperCAmelCase_      :       List[Any]          =       0
			os.makedirs(UpperCamelCase__			,       exist_ok=UpperCamelCase__		)

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

									 # Add the metadata
			UpperCAmelCase_      :       Optional[int]          =       {'''total_size''': total_size}
			UpperCAmelCase_      :       Tuple          =       {'''metadata''': metadata, '''weight_map''': weight_map}

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

			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[Any]  =					parser.parse_args()
						_UpperCamelCase ,							_UpperCamelCase						:						Any  =					shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						_UpperCamelCase						:						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)
						_UpperCamelCase						:						Optional[int]  =					NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print('Done')
						model.save_pretrained(args.pytorch_dump_folder_path)






"""simple docstring"""

def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 return x if y == 0 else greatest_common_divisor(UpperCamelCase__					, x % y			)


def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 return (x * y) // greatest_common_divisor(UpperCamelCase__					, UpperCamelCase__			)


def lowerCAmelCase_   (					UpperCamelCase__						:			int = 20			):






 """simple docstring"""







 __lowercase						=						1
 for i in range(1					, n + 1			):
  __lowercase						=						lcm(UpperCamelCase__					, UpperCamelCase__			)
 return g


if __name__ == "__main__":
       print(f"""{solution() = }""")
