



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__	):
      lowercase__     =  [
          '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__	):
      lowercase__,	lowercase__     =  emb.weight.shape
      lowercase__     =  nn.Linear(A__						,  A__						,  bias=A__	)
      lowercase__     =  emb.weight.data
      return lin_layer

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

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__ = WEIGHTS_NAME	):
      lowercase__     =  []
      lowercase__     =  0
      os.makedirs(A__						,  exist_ok=A__	)

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

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

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

      return metadata, index


if __name__ == "__main__":
       a__      :				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.",
       )
       a__      :				Optional[Any]   	=			parser.parse_args()
       a__	,		a__      :				int   	=			shard_on_the_fly(
           args.nllb_moe_checkpoint_path,
           args.pytorch_dump_folder_path,
           1_28,
           args.dtype,
       )

       a__      :				Tuple   	=			NllbMoeConfig.from_pretrained(
           "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
       )
       config.save_pretrained(args.pytorch_dump_folder_path)
       a__      :				Optional[int]   	=			NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
       print("Done")
       model.save_pretrained(args.pytorch_dump_folder_path)





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  prime_factors(A__	)
      if is_square_free(A__	):
            return -1 if len(A__	) % 2 else 1
      return 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()
