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





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any			,_lowerCamelCase						:       Union[str, Any]=None	)       ->      List[Any]:
  _lowerCAmelCase						:       Any  			=     {}
  for old_key in state_dict.keys():
    _lowerCAmelCase						:       Optional[int]  			=     old_key
    if "moe_layer.experts." in key:
      if expert_idx is not None:
        _lowerCAmelCase						:       Optional[int]  			=     key.replace("""moe_layer.experts.0"""			,f"ffn.experts.expert_{expert_idx}"	)
      else:
        _lowerCAmelCase						:       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						:       List[Any]  			=     key.replace(""".fc2."""			,""".ffn.fc2."""	)
    if "fc1" and "experts" not in key:
      _lowerCAmelCase						:       Optional[int]  			=     key.replace(""".fc1."""			,""".ffn.fc1."""	)
    if ".encoder_attn." in key:
      _lowerCAmelCase						:       List[str]  			=     key.replace(""".encoder_attn."""			,""".cross_attention."""	)
    if "encoder_attn_layer_norm" in key:
      _lowerCAmelCase						:       Union[str, Any]  			=     key.replace("""encoder_attn_layer_norm"""			,"""cross_attention_layer_norm"""	)
    if "final_layer_norm" in key:
      _lowerCAmelCase						:       Optional[int]  			=     key.replace("""final_layer_norm"""			,"""ff_layer_norm"""	)
    _lowerCAmelCase						:       Union[str, Any]  			=     state_dict[old_key]
  return new_dict





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

  for expert in range(_lowerCamelCase	):
    _lowerCAmelCase						:       Optional[int]  			=     switch_checkpoint_path + f"-rank-{expert}.pt"
    if os.path.isfile(_lowerCamelCase	):
      _lowerCAmelCase						:       List[str]  			=     torch.load(_lowerCamelCase	)["""model"""]
      remove_ignore_keys_(_lowerCamelCase	)
      _lowerCAmelCase						:       List[str]  			=     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						:       Dict  			=     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						:       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						:       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						:       int  			=     {}
  for idx, shard in enumerate(_lowerCamelCase	):
    _lowerCAmelCase						:       Optional[int]  			=     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						:       Tuple  			=     shard_file

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

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

  return metadata, index


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

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





"""simple docstring"""




from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Union[str, Any]							=		""
     _UpperCamelCase							:			str							=		"hf-legacy"  # "hf://"" is reserved for hffs






     def __init__(		self				,     a__ = None				,     a__ = None				,     **a__				,     ):
       super().__init__(self				,     **a__  )
       _lowerCAmelCase						:       Any  			=     repo_info
       _lowerCAmelCase						:       Optional[Any]  			=     token
       _lowerCAmelCase						:       Optional[int]  			=     None






     def     __A  (		self  ):
       if self.dir_cache is None:
         _lowerCAmelCase						:       Optional[Any]  			=     {}
         for hf_file in self.repo_info.siblings:
           # TODO(QL): add sizes
           _lowerCAmelCase						:       Any  			=     {
               """name""": hf_file.rfilename,
               """size""": None,
               """type""": """file""",
           }
           self.dir_cache.update(
               {
                   str(a__  ): {"""name""": str(a__  ), """size""": None, """type""": """directory"""}
                   for d in list(PurePosixPath(hf_file.rfilename  ).parents  )[:-1]
               }  )






     def     __A  (		self				,     a__				,     a__ = "rb"				,     **a__				,     ):
       if not isinstance(self.repo_info				,     a__  ):
         raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"  )
       _lowerCAmelCase						:       Tuple  			=     hf_hub_url(self.repo_info.id				,     a__				,     revision=self.repo_info.sha  )
       return fsspec.open(
           a__				,     mode=a__				,     headers=get_authentication_headers_for_url(a__				,     use_auth_token=self.token  )				,     client_kwargs={"""trust_env""": True}				,     ).open()






     def     __A  (		self				,     a__				,     **a__  ):
       self._get_dirs()
       _lowerCAmelCase						:       Union[str, Any]  			=     self._strip_protocol(a__  )
       if path in self.dir_cache:
         return self.dir_cache[path]
       else:
         raise FileNotFoundError(a__  )






     def     __A  (		self				,     a__				,     a__=False				,     **a__  ):
       self._get_dirs()
       _lowerCAmelCase						:       Any  			=     PurePosixPath(path.strip("""/"""  )  )
       _lowerCAmelCase						:       List[str]  			=     {}
       for p, f in self.dir_cache.items():
         _lowerCAmelCase						:       Any  			=     PurePosixPath(p.strip("""/"""  )  )
         _lowerCAmelCase						:       Optional[int]  			=     p.parent
         if root == path:
           _lowerCAmelCase						:       Dict  			=     f
       _lowerCAmelCase						:       Union[str, Any]  			=     list(paths.values()  )
       if detail:
         return out
       else:
         return sorted(f["""name"""] for f in out  )




