


'''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      ):
		_UpperCAmelCase						:				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(lowercase__					,       lowercase__      )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Dict	    =					emb.weight.shape
		_UpperCAmelCase						:				int	    =					nn.Linear(lowercase__					,       lowercase__					,       bias=lowercase__      )
		_UpperCAmelCase						:				Tuple	    =					emb.weight.data
		return lin_layer






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






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase = WEIGHTS_NAME      ):
		_UpperCAmelCase						:				Tuple	    =					[]
		_UpperCAmelCase						:				Optional[Any]	    =					0
		os.makedirs(lowercase__					,       exist_ok=lowercase__      )

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

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

    # Add the metadata
		_UpperCAmelCase						:				List[Any]	    =					{'''total_size''': total_size}
		_UpperCAmelCase						:				Union[str, Any]	    =					{'''metadata''': metadata, '''weight_map''': weight_map}

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

		return metadata, index


if __name__ == "__main__":
						lowerCamelCase__					=   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__					=   parser.parse_args()
						lowerCamelCase__		,lowerCamelCase__					=   shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						lowerCamelCase__					=   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__					=   NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print('Done')
						model.save_pretrained(args.pytorch_dump_folder_path)






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      if isinstance(lowercase__   ,	lowercase__						):
            __lowerCAmelCase					:						Dict					=					np.full((len(lowercase__						), sequence_length, 2)   ,	lowercase__						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					np.full((len(lowercase__						), sequence_length)   ,	lowercase__						)

      for i, tensor in enumerate(lowercase__						):
            if padding_side == "right":
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						int					=					tensor[:sequence_length]
            else:
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						Optional[Any]					=					tensor[:sequence_length]

      return out_tensor.tolist()







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					ord(lowercase__						)
      if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
            return True
      __lowerCAmelCase					:						int					=					unicodedata.category(lowercase__						)
      if cat.startswith('''P'''						):
            return True
      return False







@dataclass
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  True
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  -100
 _UpperCamelCase			  =  "pt"
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       import torch

       __lowerCAmelCase					:						List[str]					=					'''label''' if '''label''' in features[0].keys() else '''labels'''
       __lowerCAmelCase					:						Union[str, Any]					=					[feature[label_name] for feature in features] if label_name in features[0].keys() else None
       __lowerCAmelCase					:						List[Any]					=					self.tokenizer.pad(
           A_      ,							padding=self.padding      ,							max_length=self.max_length      ,							pad_to_multiple_of=self.pad_to_multiple_of      ,							return_tensors='''pt''' if labels is None else None      ,							)

       if labels is None:
             return batch

       __lowerCAmelCase					:						Dict					=					torch.tensor(batch['''entity_ids''']					).shape[1]
       __lowerCAmelCase					:						Optional[int]					=					self.tokenizer.padding_side
       if padding_side == "right":
             __lowerCAmelCase					:						Any					=					[
                 list(A_					) + [self.label_pad_token_id] * (sequence_length - len(A_					)) for label in labels
             ]
       else:
             __lowerCAmelCase					:						Optional[int]					=					[
                 [self.label_pad_token_id] * (sequence_length - len(A_					)) + list(A_					) for label in labels
             ]

       __lowerCAmelCase					:						Tuple					=					[feature['''ner_tags'''] for feature in features]
       __lowerCAmelCase					:						List[Any]					=					padding_tensor(A_      ,							-1      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[int]					=					[feature['''original_entity_spans'''] for feature in features]
       __lowerCAmelCase					:						Any					=					padding_tensor(A_      ,							(-1, -1)      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[Any]					=					{k: torch.tensor(A_      ,							dtype=torch.intaa					) for k, v in batch.items()}

       return batch


