"""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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple			) ->   Any:
  _lowerCAmelCase		: Optional[int]						    =  [
      """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(_lowerCamelCase    ,_lowerCamelCase			)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Dict			) ->   Union[str, Any]:
  _lowerCAmelCase    ,			_lowerCAmelCase		: int						    =  emb.weight.shape
  _lowerCAmelCase		: Any						    =  nn.Linear(_lowerCamelCase    ,_lowerCamelCase    ,bias=_lowerCamelCase			)
  _lowerCAmelCase		: int						    =  emb.weight.data
  return lin_layer





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Tuple=None			) ->   Tuple:
  _lowerCAmelCase		: Any						    =  {}
  for old_key in state_dict.keys():
    _lowerCAmelCase		: str						    =  old_key
    if "moe_layer.experts." in key:
      if expert_idx is not None:
        _lowerCAmelCase		: Union[str, Any]						    =  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		: Optional[int]						    =  key.replace(""".moe_layer.gate.wg"""    ,""".ffn.router.classifier"""			)
    if "fc2" and "experts" not in key:
      _lowerCAmelCase		: str						    =  key.replace(""".fc2."""    ,""".ffn.fc2."""			)
    if "fc1" and "experts" not in key:
      _lowerCAmelCase		: List[str]						    =  key.replace(""".fc1."""    ,""".ffn.fc1."""			)
    if ".encoder_attn." in key:
      _lowerCAmelCase		: str						    =  key.replace(""".encoder_attn."""    ,""".cross_attention."""			)
    if "encoder_attn_layer_norm" in key:
      _lowerCAmelCase		: str						    =  key.replace("""encoder_attn_layer_norm"""    ,"""cross_attention_layer_norm"""			)
    if "final_layer_norm" in key:
      _lowerCAmelCase		: Any						    =  key.replace("""final_layer_norm"""    ,"""ff_layer_norm"""			)
    _lowerCAmelCase		: Dict						    =  state_dict[old_key]
  return new_dict





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Tuple    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: str = WEIGHTS_NAME			) ->   Dict:
  _lowerCAmelCase		: Optional[Any]						    =  []
  _lowerCAmelCase		: List[str]						    =  0
  os.makedirs(_lowerCamelCase    ,exist_ok=_lowerCamelCase			)

  for expert in range(_lowerCamelCase			):
    _lowerCAmelCase		: str						    =  switch_checkpoint_path + f"-rank-{expert}.pt"
    if os.path.isfile(_lowerCamelCase			):
      _lowerCAmelCase		: int						    =  torch.load(_lowerCamelCase			)["""model"""]
      remove_ignore_keys_(_lowerCamelCase			)
      _lowerCAmelCase		: Optional[int]						    =  rename_fairseq_keys(_lowerCamelCase    ,_lowerCamelCase			)
      _lowerCAmelCase		: Dict						    =  os.path.join(
          _lowerCamelCase    ,weights_name.replace(""".bin"""    ,f"-{len(_lowerCamelCase			)+1:05d}-of-???.bin"			)			)
      torch.save(_lowerCamelCase    ,_lowerCamelCase			)
      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(_lowerCamelCase			)[0]].dtype			)

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

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

  with open(os.path.join(_lowerCamelCase    ,_lowerCamelCase			)    ,"""w"""    ,encoding="""utf-8"""			) as f:
    _lowerCAmelCase		: List[Any]						    =  json.dumps(_lowerCamelCase    ,indent=2    ,sort_keys=_lowerCamelCase			) + """\n"""
    f.write(_lowerCamelCase			)

  return metadata, index


if __name__ == "__main__":
     _a   :	str 					=     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   :	str 					=     parser.parse_args()
     _a					,     _a   :	Union[str, Any] 					=     shard_on_the_fly(
         args.nllb_moe_checkpoint_path,
         args.pytorch_dump_folder_path,
         128,
         args.dtype,
     )

     _a   :	str 					=     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   :	Dict 					=     NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
     print('Done')
     model.save_pretrained(args.pytorch_dump_folder_path)





"""simple docstring"""




from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class 		__A							:
     _UpperCamelCase							:    int
     _UpperCamelCase							:    Node | None	      =	None
     _UpperCamelCase							:    Node | None	      =	None







def 							SCREAMING_SNAKE_CASE  (    ) ->   Node | None:
  _lowerCAmelCase		: Tuple						    =  Node(1			)
  _lowerCAmelCase		: int						    =  Node(2			)
  _lowerCAmelCase		: int						    =  Node(3			)
  _lowerCAmelCase		: Any						    =  Node(4			)
  _lowerCAmelCase		: Dict						    =  Node(5			)
  return tree





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return [root.data, *preorder(root.left			), *preorder(root.right			)] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return postorder(root.left			) + postorder(root.right			) + [root.data] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return [*inorder(root.left			), root.data, *inorder(root.right			)] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   int:
  return (max(height(root.left			)    ,height(root.right			)			) + 1) if root else 0





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  if root is None:
    return output

  _lowerCAmelCase		: Union[str, Any]						    =  deque([root]			)

  while process_queue:
    _lowerCAmelCase		: Optional[Any]						    =  process_queue.popleft()
    output.append(node.data			)

    if node.left:
      process_queue.append(node.left			)
    if node.right:
      process_queue.append(node.right			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  def populate_output(_lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) -> None:
    if not root:
      return
    if level == 1:
      output.append(root.data			)
    elif level > 1:
      populate_output(root.left    ,level - 1			)
      populate_output(root.right    ,level - 1			)

  populate_output(_lowerCamelCase    ,_lowerCamelCase			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  def populate_output(_lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) -> None:
    if root is None:
      return
    if level == 1:
      output.append(root.data			)
    elif level > 1:
      populate_output(root.right    ,level - 1			)
      populate_output(root.left    ,level - 1			)

  populate_output(_lowerCamelCase    ,_lowerCamelCase			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   Sequence[Node | None] | list[Any]:
  if root is None:
    return []

  _lowerCAmelCase		: list[Sequence[Node | None]]						    =  []

  _lowerCAmelCase		: Tuple						    =  0
  _lowerCAmelCase		: Dict						    =  height(_lowerCamelCase			)

  for h in range(1    ,height_tree + 1			):
    if not flag:
      output.append(get_nodes_from_left_to_right(_lowerCamelCase    ,_lowerCamelCase			)			)
      _lowerCAmelCase		: Any						    =  1
    else:
      output.append(get_nodes_from_right_to_left(_lowerCamelCase    ,_lowerCamelCase			)			)
      _lowerCAmelCase		: Optional[int]						    =  0

  return output





def 							SCREAMING_SNAKE_CASE  (    ) ->   None:  # Main function for testing.
  _lowerCAmelCase		: int						    =  make_tree()
  print(f"In-order Traversal: {inorder(_lowerCamelCase			)}"			)
  print(f"Pre-order Traversal: {preorder(_lowerCamelCase			)}"			)
  print(f"Post-order Traversal: {postorder(_lowerCamelCase			)}"    ,"""\n"""			)

  print(f"Height of Tree: {height(_lowerCamelCase			)}"    ,"""\n"""			)

  print("""Complete Level Order Traversal: """			)
  print(level_order(_lowerCamelCase			)    ,"""\n"""			)

  print("""Level-wise order Traversal: """			)

  for level in range(1    ,height(_lowerCamelCase			) + 1			):
    print(f"Level {level}:"    ,get_nodes_from_left_to_right(_lowerCamelCase    ,level=_lowerCamelCase			)			)

  print("""\nZigZag order Traversal: """			)
  print(zigzag(_lowerCamelCase			)			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




