






'''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						:	Union[str, Any] ):
							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(__snake_case  ,					__snake_case )

def _UpperCAmelCase   (	a						:	Optional[int] ):
							snake_case__	  =       emb.weight.shape
							snake_case__	  =       nn.Linear(__snake_case  ,					__snake_case  ,					bias=__snake_case )
							snake_case__	  =       emb.weight.data
							return lin_layer

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

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Optional[Any]  ,					a						:	Tuple  ,					a						:	List[Any]  ,					a						:	str = WEIGHTS_NAME ):
							snake_case__	  =       []
							snake_case__	  =       0
							os.makedirs(__snake_case  ,					exist_ok=__snake_case )

							for expert in range(__snake_case ):
														snake_case__	  =       switch_checkpoint_path + F'''-rank-{expert}.pt'''
														if os.path.isfile(__snake_case ):
																					snake_case__	  =       torch.load(__snake_case )["model"]
																					remove_ignore_keys_(__snake_case )
																					snake_case__	  =       rename_fairseq_keys(__snake_case  ,					__snake_case )
																					snake_case__	  =       os.path.join(
																					    __snake_case  ,					weights_name.replace(""".bin"""  ,					F'''-{len(__snake_case )+1:05d}-of-???.bin''' ) )
																					torch.save(__snake_case  ,					__snake_case )
																					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(__snake_case )[0]].dtype )

    # Add the last block
							snake_case__	  =       os.path.join(__snake_case  ,					weights_name.replace(""".bin"""  ,					F'''-{len(__snake_case )+1:05d}-of-???.bin''' ) )
							snake_case__	  =       torch.load(switch_checkpoint_path + """-shared.pt""" )["model"]
							remove_ignore_keys_(__snake_case )
							snake_case__	  =       rename_fairseq_keys(__snake_case  ,					__snake_case )
							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(__snake_case ) == 1:
														snake_case__	  =       os.path.join(__snake_case  ,					__snake_case )
														torch.save(__snake_case  ,					__snake_case )
														return {weights_name: sharded_state_dicts[0]}, None
							else:
														torch.save(__snake_case  ,					__snake_case )
							# Otherwise, let's build the index
							snake_case__	  =       {}
							for idx, shard in enumerate(__snake_case ):
														snake_case__	  =       weights_name.replace(""".bin"""  ,					F'''-{idx+1:05d}-of-{len(__snake_case ):05d}.bin''' )
														snake_case__	  =       os.path.join(__snake_case  ,					weights_name.replace(""".bin"""  ,					F'''-{idx+1:05d}-of-???.bin''' ) )
														os.rename(__snake_case  ,					os.path.join(__snake_case  ,					__snake_case ) )
														for key in shard:
																					snake_case__	  =       shard_file

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

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

							return metadata, index


if __name__ == "__main__":
	a__       =     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__       =     parser.parse_args()
	a__       =     shard_on_the_fly(
	    args.nllb_moe_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    1_2_8,
	    args.dtype,
	)

	a__       =     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__       =     NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
	print("""Done""")
	model.save_pretrained(args.pytorch_dump_folder_path)








def _UpperCAmelCase   (	a						:	int = 400_0000 ):
							snake_case__	  =       [0, 1]
							snake_case__	  =       0
							while fib[i] <= n:
														fib.append(fib[i] + fib[i + 1] )
														if fib[i + 2] > n:
																					break
														i += 1
							snake_case__	  =       0
							for j in range(len(a ) - 1 ):
														if fib[j] % 2 == 0:
																					total += fib[j]

							return total


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