





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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Any     )      ->						Dict:
  _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(snake_case__			,							snake_case__     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict     )      ->						Optional[Any]:
  _lowercase						,		_lowercase							= emb.weight.shape
  _lowercase							= nn.Linear(snake_case__			,							snake_case__			,							bias=snake_case__     )
  _lowercase							= emb.weight.data
  return lin_layer

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :Any=None     )      ->						int:
  _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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int			,							snake_case__  :str			,							snake_case__  :Union[str, Any]			,							snake_case__  :List[str]			,							snake_case__  :str = WEIGHTS_NAME     )      ->						List[str]:
  _lowercase							= []
  _lowercase							= 0
  os.makedirs(snake_case__			,							exist_ok=snake_case__     )

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

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

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

  return metadata, index


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

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






from itertools import count

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int = 50     )      ->						int:
  _lowercase							= [1] * min_block_length

  for n in count(snake_case__     ):
    fill_count_functions.append(1     )

    for block_length in range(snake_case__			,							n + 1     ):
      for block_start in range(n - block_length     ):
        fill_count_functions[n] += fill_count_functions[
            n - block_start - block_length - 1
        ]

      fill_count_functions[n] += 1

    if fill_count_functions[n] > 100_0000:
      break

  return n


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