
"""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   __magic_name__	(      __snake_case				:       Union[str, Any]  ) ->				str:
       lowercase    :				List[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(__snake_case     , __snake_case  )


def   __magic_name__	(      __snake_case				:       Optional[Any]  ) ->				Dict:
       lowercase		,    lowercase    :				Dict       		=							emb.weight.shape
       lowercase    :				Union[str, Any]       		=							nn.Linear(__snake_case     , __snake_case     , bias=__snake_case  )
       lowercase    :				Tuple       		=							emb.weight.data
       return lin_layer


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


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       Optional[Any]     , __snake_case				:       Dict     , __snake_case				:       List[str]     , __snake_case				:       str = WEIGHTS_NAME  ) ->				int:
       lowercase    :				List[Any]       		=							[]
       lowercase    :				Dict       		=							0
       os.makedirs(__snake_case     , exist_ok=__snake_case  )

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

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

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

       with open(os.path.join(__snake_case     , __snake_case  )     , "w"     , encoding="utf-8"  ) as f:
              lowercase    :				Optional[int]       		=							json.dumps(__snake_case     , indent=2     , sort_keys=__snake_case  ) + "\n"
              f.write(__snake_case  )

       return metadata, index


if __name__ == "__main__":
   _A			:       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.""",
   )
   _A			:       List[str]   		=       parser.parse_args()
   _A		,     _A			:       Optional[Any]   		=       shard_on_the_fly(
       args.nllb_moe_checkpoint_path,
       args.pytorch_dump_folder_path,
       1_28,
       args.dtype,
   )

   _A			:       List[Any]   		=       NllbMoeConfig.from_pretrained(
       """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
   )
   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"""




import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A			:       List[str]   		=       logging.get_logger(__name__)

_A			:       List[str]   		=       {"""vocab_file""": """spiece.model"""}

_A			:       List[Any]   		=       {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

_A			:       str   		=       {
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}

class 		a__						(      a_						):
   __lowerCAmelCase            =				VOCAB_FILES_NAMES
   __lowerCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase            =				["""input_ids""", """attention_mask"""]
   __lowerCAmelCase            =				[]
   def __init__(			self	,    _a	,    _a="<unk>"	,    _a="<s>"	,    _a="</s>"	,    _a="<pad>"	,    _a="[SEP]"	,    _a="[MASK]"	,    _a="[CLS]"	,    _a = None	,    **_a	,    ):
          lowercase    :				Dict       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else bos_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else eos_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else unk_token
          lowercase    :				List[Any]       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else pad_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else cls_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else sep_token

          # Mask token behave like a normal word, i.e. include the space before it
          lowercase    :				List[Any]       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else mask_token

          lowercase    :				Optional[int]       		=							{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=_a	,    eos_token=_a	,    unk_token=_a	,    pad_token=_a	,    sep_token=_a	,    mask_token=_a	,    cls_token=_a	,    sp_model_kwargs=self.sp_model_kwargs	,    **_a	,    )

          lowercase    :				Optional[Any]       		=							vocab_file

          lowercase    :				List[str]       		=							spm.SentencePieceProcessor(**self.sp_model_kwargs				)
          self.sp_model.Load(_a				)
   @property
   def 			__magic_name__			(			self				):
          return self.sp_model.get_piece_size()
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							{self.convert_ids_to_tokens(_a				): i for i in range(self.vocab_size				)}
          vocab.update(self.added_tokens_encoder				)
          return vocab
   def __getstate__(			self				):
          lowercase    :				Union[str, Any]       		=							self.__dict__.copy()
          lowercase    :				Dict       		=							None
          return state
   def __setstate__(			self	,    _a				):
          lowercase    :				str       		=							d

          # for backward compatibility
          if not hasattr(self	,    "sp_model_kwargs"				):
                 lowercase    :				List[str]       		=							{}

          lowercase    :				Any       		=							spm.SentencePieceProcessor(**self.sp_model_kwargs				)
          self.sp_model.Load(self.vocab_file				)
   def 			__magic_name__			(			self	,    _a				):
          return self.sp_model.encode(_a	,    out_type=_a				)
   def 			__magic_name__			(			self	,    _a				):
          return self.sp_model.piece_to_id(_a				)
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				Union[str, Any]       		=							self.sp_model.IdToPiece(_a				)
          return token
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				List[Any]       		=							[]
          lowercase    :				List[Any]       		=							""
          lowercase    :				List[Any]       		=							False
          for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                        if not prev_is_special:
                               out_string += " "
                        out_string += self.sp_model.decode(_a				) + token
                        lowercase    :				int       		=							True
                        lowercase    :				Union[str, Any]       		=							[]
                 else:
                        current_sub_tokens.append(_a				)
                        lowercase    :				int       		=							False
          out_string += self.sp_model.decode(_a				)
          return out_string.strip()
   def 			__magic_name__			(			self	,    _a	,    _a = False	,    _a = None	,    _a = True	,    **_a	,    ):
          lowercase    :				int       		=							kwargs.pop("use_source_tokenizer"	,    _a				)

          lowercase    :				Union[str, Any]       		=							self.convert_ids_to_tokens(_a	,    skip_special_tokens=_a				)

          # To avoid mixing byte-level and unicode for byte-level BPT
          # we need to build string separately for added tokens and byte-level tokens
          # cf. https://github.com/huggingface/transformers/issues/1133
          lowercase    :				Any       		=							[]
          lowercase    :				Dict       		=							[]
          for token in filtered_tokens:
                 if skip_special_tokens and token in self.all_special_ids:
                        continue
                 if token in self.added_tokens_encoder:
                        if current_sub_text:
                               sub_texts.append(self.convert_tokens_to_string(_a				)				)
                               lowercase    :				int       		=							[]
                        sub_texts.append(_a				)
                 else:
                        current_sub_text.append(_a				)
          if current_sub_text:
                 sub_texts.append(self.convert_tokens_to_string(_a				)				)

          # Mimic the behavior of the Rust tokenizer:
          # No space before [MASK] and [SEP]
          if spaces_between_special_tokens:
                 lowercase    :				Dict       		=							re.sub(R" (\[(MASK|SEP)\])"	,    R"\1"	,    " ".join(_a				)				)
          else:
                 lowercase    :				Union[str, Any]       		=							"".join(_a				)

          lowercase    :				int       		=							(
              clean_up_tokenization_spaces
              if clean_up_tokenization_spaces is not None
              else self.clean_up_tokenization_spaces
          )
          if clean_up_tokenization_spaces:
                 lowercase    :				Union[str, Any]       		=							self.clean_up_tokenization(_a				)
                 return clean_text
          else:
                 return text
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if not os.path.isdir(_a				):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
                 return
          lowercase    :				List[Any]       		=							os.path.join(
              _a	,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

          if os.path.abspath(self.vocab_file				) != os.path.abspath(_a				) and os.path.isfile(self.vocab_file				):
                 copyfile(self.vocab_file	,    _a				)
          elif not os.path.isfile(self.vocab_file				):
                 with open(_a	,    "wb"				) as fi:
                        lowercase    :				int       		=							self.sp_model.serialized_model_proto()
                        fi.write(_a				)

          return (out_vocab_file,)
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          lowercase    :				List[str]       		=							[self.cls_token_id]
          lowercase    :				Tuple       		=							[self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep
   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = False				):
          if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=_a	,    token_ids_a=_a	,    already_has_special_tokens=_a				)

          if token_ids_a is None:
                 return [1] + ([0] * len(_a				)) + [1]
          return [1] + ([0] * len(_a				)) + [1] + ([0] * len(_a				)) + [1]

   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Tuple       		=							[self.sep_token_id]
          lowercase    :				Tuple       		=							[self.cls_token_id]
          if token_ids_a is None:
                 return len(cls + token_ids_a + sep				) * [0]
          return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]


