
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							:	Optional[int]     ):
			lowerCAmelCase__			:     List[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   _a  ( __UpperCamelCase							:	Optional[Any]     ):
			lowerCAmelCase__   ,					lowerCAmelCase__			:     Union[str, Any]					   =      emb.weight.shape
			lowerCAmelCase__			:     Optional[Any]					   =      nn.Linear(__a						,__a						,bias=__a     )
			lowerCAmelCase__			:     Tuple					   =      emb.weight.data
			return lin_layer



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



def   _a  ( __UpperCamelCase							:	Union[str, Any]						,__UpperCamelCase							:	Dict						,__UpperCamelCase							:	str						,__UpperCamelCase							:	List[str]						,__UpperCamelCase							:	str = WEIGHTS_NAME     ):
			lowerCAmelCase__			:     List[Any]					   =      []
			lowerCAmelCase__			:     Optional[Any]					   =      0
			os.makedirs(__a						,exist_ok=__a     )

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

    # Add the metadata
			lowerCAmelCase__			:     List[str]					   =      {'''total_size''': total_size}
			lowerCAmelCase__			:     Dict					   =      {'''metadata''': metadata, '''weight_map''': weight_map}

			with open(os.path.join(__a						,__a     )						,'''w'''						,encoding='''utf-8'''     ) as f:
						lowerCAmelCase__			:     Tuple					   =      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__							: List[str]     =       parser.parse_args()
							A__							: List[str]     =       shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    1_2_8,
							    args.dtype,
							)

							A__							: int     =       NllbMoeConfig.from_pretrained(
							    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
							)
							config.save_pretrained(args.pytorch_dump_folder_path)
							A__							: str     =       NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print("""Done""")
							model.save_pretrained(args.pytorch_dump_folder_path)










def   __lowerCamelCase   (				__a  :int       ,	__a  :int       )						->							Optional[Any]:






		"""simple docstring"""

		if b == 0:
				return 1
		if (b % 2) == 0:
				return actual_power(__a       ,	int(b / 2       )       ) * actual_power(__a       ,	int(b / 2       )       )
		else:
				return a * actual_power(__a       ,	int(b / 2       )       ) * actual_power(__a       ,	int(b / 2       )       )







def   __lowerCamelCase   (				__a  :int       ,	__a  :int       )						->							float:






		"""simple docstring"""

		if b < 0:
				return 1 / actual_power(__a       ,	__a       )
		return actual_power(__a       ,	__a       )


if __name__ == "__main__":
					print(power(-2, -3))



