


"""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        lowerCamelCase_(		_lowerCamelCase       )    ->     Any:
   '''simple docstring'''





   _lowerCamelCase   :				Any							=     [
       "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        lowerCamelCase_(		_lowerCamelCase       )    ->     str:
   '''simple docstring'''





   _lowerCamelCase,					_lowerCamelCase   :				List[str]							=     emb.weight.shape
   _lowerCamelCase   :				Dict							=     nn.Linear(_lowerCamelCase			,		_lowerCamelCase			,		bias=_lowerCamelCase       )
   _lowerCamelCase   :				Any							=     emb.weight.data
   return lin_layer







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase=None       )    ->     Any:
   '''simple docstring'''





   _lowerCamelCase   :				Union[str, Any]							=     {}
   for old_key in state_dict.keys():
      _lowerCamelCase   :				List[str]							=     old_key
      if "moe_layer.experts." in key:
         if expert_idx is not None:
            _lowerCamelCase   :				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   :				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   :				Union[str, Any]							=     key.replace(".fc1."			,		".ffn.fc1."       )
      if ".encoder_attn." in key:
         _lowerCamelCase   :				Union[str, Any]							=     key.replace(".encoder_attn."			,		".cross_attention."       )
      if "encoder_attn_layer_norm" in key:
         _lowerCamelCase   :				int							=     key.replace("encoder_attn_layer_norm"			,		"cross_attention_layer_norm"       )
      if "final_layer_norm" in key:
         _lowerCamelCase   :				Tuple							=     key.replace("final_layer_norm"			,		"ff_layer_norm"       )
      _lowerCamelCase   :				List[str]							=     state_dict[old_key]
   return new_dict







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase = WEIGHTS_NAME       )    ->     List[str]:
   '''simple docstring'''





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

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

    # Add the metadata
   _lowerCamelCase   :				Union[str, Any]							=     {"total_size": total_size}
   _lowerCamelCase   :				Optional[Any]							=     {"metadata": metadata, "weight_map": weight_map}

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

   return metadata, index


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

  _lowerCAmelCase							:    List[Any]					   =	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)
  _lowerCAmelCase							:    Union[str, Any]					   =	NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
  print('''Done''')
  model.save_pretrained(args.pytorch_dump_folder_path)




from __future__ import annotations

from fractions import Fraction


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   bool:
			return (
			    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
			)


def A    (							__UpperCamelCase       )    ->   list[str]:
			A__	  =     []
			A__	  =     11
			A__	  =     int('1' + '0' * digit_len       )
			for num in range(__UpperCamelCase			,						__UpperCamelCase       ):
						while den <= 99:
									if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
												if is_digit_cancelling(__UpperCamelCase			,						__UpperCamelCase       ):
															solutions.append(f'''{num}/{den}'''       )
									den += 1
						num += 1
						A__	  =     10
			return solutions


def A    (							__UpperCamelCase = 2       )    ->   int:
			A__	  =     1.0
			for fraction in fraction_list(__UpperCamelCase       ):
						A__	  =     Fraction(__UpperCamelCase       )
						result *= frac.denominator / frac.numerator
			return int(__UpperCamelCase       )


if __name__ == "__main__":
							print(solution())




