





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		)     ->		Dict:







  """simple docstring"""





  lowerCamelCase__:      str			  =[
      "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		)     ->		List[str]:







  """simple docstring"""





  lowerCamelCase__  ,					lowerCamelCase__:      Tuple			  =emb.weight.shape
  lowerCamelCase__:      Tuple			  =nn.Linear(__a	,    __a	,    bias=__a		)
  lowerCamelCase__:      int			  =emb.weight.data
  return lin_layer





def      lowerCAmelCase_ (       __a	,    __a=None		)     ->		Tuple:







  """simple docstring"""





  lowerCamelCase__:      Optional[Any]			  ={}
  for old_key in state_dict.keys():
    lowerCamelCase__:      List[Any]			  =old_key
    if "moe_layer.experts." in key:
      if expert_idx is not None:
        lowerCamelCase__:      int			  =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__:      int			  =key.replace(".moe_layer.gate.wg"	,    ".ffn.router.classifier"		)
    if "fc2" and "experts" not in key:
      lowerCamelCase__:      Tuple			  =key.replace(".fc2."	,    ".ffn.fc2."		)
    if "fc1" and "experts" not in key:
      lowerCamelCase__:      Tuple			  =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__:      Optional[int]			  =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__:      List[str]			  =state_dict[old_key]
  return new_dict





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a = WEIGHTS_NAME		)     ->		List[str]:







  """simple docstring"""





  lowerCamelCase__:      List[str]			  =[]
  lowerCamelCase__:      Any			  =0
  os.makedirs(__a	,    exist_ok=__a		)

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

    # Add the metadata
  lowerCamelCase__:      List[str]			  ={"total_size": total_size}
  lowerCamelCase__:      List[Any]			  ={"metadata": metadata, "weight_map": weight_map}

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

  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					,	__A									=				shard_on_the_fly(
         args.nllb_moe_checkpoint_path,
         args.pytorch_dump_folder_path,
         128,
         args.dtype,
     )

     __A									=				NllbMoeConfig.from_pretrained(
         "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
     )
     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)








from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def      lowerCAmelCase_ (       )     ->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__  ,					lowerCamelCase__:      int			  =9, 14  # noqa: F841
  lowerCamelCase__:      List[Any]			  =[
      [0, 1, 4],
      [0, 7, 8],
      [1, 2, 8],
      [7, 8, 7],
      [7, 6, 1],
      [2, 8, 2],
      [8, 6, 6],
      [2, 3, 7],
      [2, 5, 4],
      [6, 5, 2],
      [3, 5, 14],
      [3, 4, 9],
      [5, 4, 10],
      [1, 7, 11],
  ]

  lowerCamelCase__:      List[str]			  =defaultdict(__a		)
  for nodea, nodea, cost in edges:
    adjancency[nodea].append([nodea, cost]		)
    adjancency[nodea].append([nodea, cost]		)

  lowerCamelCase__:      List[str]			  =mst(__a		)

  lowerCamelCase__:      Union[str, Any]			  =[
      [7, 6, 1],
      [2, 8, 2],
      [6, 5, 2],
      [0, 1, 4],
      [2, 5, 4],
      [2, 3, 7],
      [0, 7, 8],
      [3, 4, 9],
  ]

  for answer in expected:
    lowerCamelCase__:      Optional[int]			  =tuple(answer[:2]		)
    lowerCamelCase__:      List[Any]			  =tuple(edge[::-1]		)
    assert edge in result or reverse in result

