





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      _A       (		SCREAMING_SNAKE_CASE     :   List[str]						):





  """simple docstring"""



  a__	:					Tuple										=[
      '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(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



  a__	:					Union[str, Any]										=emb.weight.shape
  a__	:					str										=nn.Linear(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       bias=SCREAMING_SNAKE_CASE						)
  a__	:					str										=emb.weight.data
  return lin_layer
def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[int]       ,       SCREAMING_SNAKE_CASE     :   Union[str, Any]=None						):





  """simple docstring"""



  a__	:					Any										={}
  for old_key in state_dict.keys():
    a__	:					Tuple										=old_key
    if "moe_layer.experts." in key:
      if expert_idx is not None:
        a__	:					int										=key.replace("moe_layer.experts.0"       ,       f'''ffn.experts.expert_{expert_idx}'''						)
      else:
        a__	:					Any										=key.replace("moe_layer.experts."       ,       "ffn.experts.expert_"						)
    if "gate" in key:
      a__	:					Dict										=key.replace(".moe_layer.gate.wg"       ,       ".ffn.router.classifier"						)
    if "fc2" and "experts" not in key:
      a__	:					str										=key.replace(".fc2."       ,       ".ffn.fc2."						)
    if "fc1" and "experts" not in key:
      a__	:					str										=key.replace(".fc1."       ,       ".ffn.fc1."						)
    if ".encoder_attn." in key:
      a__	:					Tuple										=key.replace(".encoder_attn."       ,       ".cross_attention."						)
    if "encoder_attn_layer_norm" in key:
      a__	:					Tuple										=key.replace("encoder_attn_layer_norm"       ,       "cross_attention_layer_norm"						)
    if "final_layer_norm" in key:
      a__	:					Optional[int]										=key.replace("final_layer_norm"       ,       "ff_layer_norm"						)
    a__	:					Dict										=state_dict[old_key]
  return new_dict
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   List[str]       ,       SCREAMING_SNAKE_CASE     :   str = WEIGHTS_NAME						):





  """simple docstring"""



  a__	:					Dict										=[]
  a__	:					str										=0
  os.makedirs(SCREAMING_SNAKE_CASE       ,       exist_ok=SCREAMING_SNAKE_CASE						)

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

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

    # Add the metadata
  a__	:					int										={'total_size': total_size}
  a__	:					Dict										={'metadata': metadata, 'weight_map': weight_map}

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

  return metadata, index


if __name__ == "__main__":
     UpperCAmelCase	:	int		     =       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.""",
     )
     UpperCAmelCase	:	Optional[Any]		     =       parser.parse_args()
     UpperCAmelCase	:	Optional[Any]		     =       shard_on_the_fly(
         args.nllb_moe_checkpoint_path,
         args.pytorch_dump_folder_path,
         128,
         args.dtype,
     )

     UpperCAmelCase	:	List[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)
     UpperCAmelCase	:	int		     =       NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
     print("""Done""")
     model.save_pretrained(args.pytorch_dump_folder_path)











def        lowercase_							(A : Union[str, Any]		,					A : List[str]		,					A : int		,					A : Optional[int]      ):
       global f  # a global dp table for knapsack
       if f[i][j] < 0:
              if j < wt[i - 1]:
                     snake_case__							:	Union[str, Any]   	=	mf_knapsack(i - 1		,					A		,					A		,					A      )
              else:
                     snake_case__							:	Any   	=	max(
                         mf_knapsack(i - 1		,					A		,					A		,					A      )		,					mf_knapsack(i - 1		,					A		,					A		,					j - wt[i - 1]      ) + val[i - 1]		,					)
              snake_case__							:	Optional[int]   	=	val
       return f[i][j]
def        lowercase_							(A : Optional[int]		,					A : Union[str, Any]		,					A : str		,					A : Dict      ):
       snake_case__							:	int   	=	[[0] * (w + 1) for _ in range(n + 1      )]

       for i in range(1		,					n + 1      ):
              for w_ in range(1		,					w + 1      ):
                     if wt[i - 1] <= w_:
                            snake_case__							:	Union[str, Any]   	=	max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]		,					dp[i - 1][w_]      )
                     else:
                            snake_case__							:	str   	=	dp[i - 1][w_]

       return dp[n][w_], dp
def        lowercase_							(A : int		,					A : list		,					A : list      ):
       if not (isinstance(A		,					(list, tuple)      ) and isinstance(A		,					(list, tuple)      )):
              raise ValueError(
                  'Both the weights and values vectors must be either lists or tuples'      )

       snake_case__							:	Dict   	=	len(A      )
       if num_items != len(A      ):
              snake_case__							:	str   	=	(
                  'The number of weights must be the same as the number of values.\n'
                  F'''But got {num_items} weights and {len(A      )} values'''
              )
              raise ValueError(A      )
       for i in range(A      ):
              if not isinstance(wt[i]		,					A      ):
                     snake_case__							:	Optional[int]   	=	(
                         'All weights must be integers but got weight of '
                         F'''type {type(wt[i]      )} at index {i}'''
                     )
                     raise TypeError(A      )

       snake_case__	,				snake_case__							:	Optional[int]   	=	knapsack(A		,					A		,					A		,					A      )
       snake_case__							:	set   	=	set()
       _construct_solution(A		,					A		,					A		,					A		,					A      )

       return optimal_val, example_optional_set
def        lowercase_							(A : list		,					A : list		,					A : int		,					A : int		,					A : set      ):
       # for the current item i at a maximum weight j to be part of an optimal subset,
       # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
       # where i - 1 means considering only the previous items at the given maximum weight
       if i > 0 and j > 0:
              if dp[i - 1][j] == dp[i][j]:
                     _construct_solution(A		,					A		,					i - 1		,					A		,					A      )
              else:
                     optimal_set.add(A      )
                     _construct_solution(A		,					A		,					i - 1		,					j - wt[i - 1]		,					A      )


if __name__ == "__main__":
 a_   :Any        =      [3, 2, 4, 4]
 a_   :List[Any]        =      [4, 3, 2, 3]
 a_   :Union[str, Any]        =      4
 a_   :List[str]        =      6
 a_   :Union[str, Any]        =      [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
 a_  ,						a_   :List[Any]        =      knapsack(w, wt, val, n)
 print(optimal_solution)
 print(mf_knapsack(n, wt, val, w))  # switched the n and w

 # testing the dynamic programming problem with example
 # the optimal subset for the above example are items 3 and 4
 a_  ,						a_   :Any        =      knapsack_with_example_solution(w, wt, val)
 assert optimal_solution == 8
 assert optimal_subset == {3, 4}
 print("optimal_value = ", optimal_solution)
 print("An optimal subset corresponding to the optimal value", optimal_subset)






