



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__      :				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)





def   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      if principal <= 0:
            raise Exception('Principal borrowed must be > 0'	)
      if rate_per_annum < 0:
            raise Exception('Rate of interest must be >= 0'	)
      if years_to_repay <= 0 or not isinstance(A__						,  A__	):
            raise Exception('Years to repay must be an integer > 0'	)

      # Yearly rate is divided by 12 to get monthly rate
      lowercase__     =  rate_per_annum / 12

      # Years to repay is multiplied by 12 to get number of payments as payment is monthly
      lowercase__     =  years_to_repay * 12

      return (
          principal
          * rate_per_month
          * (1 + rate_per_month) ** number_of_payments
          / ((1 + rate_per_month) ** number_of_payments - 1)
      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
