



import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


a_   :Union[str, Any]        =      logging.get_logger(__name__)




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""AutoTokenizer"""
     _SCREAMING_SNAKE_CASE						=	["""tokenizer"""]

     _SCREAMING_SNAKE_CASE						=	{
         """semantic_prompt""": 1,
         """coarse_prompt""": 2,
         """fine_prompt""": 2,
     }






     def __init__(       self  :  Any, _snake_case  :  int, _snake_case  :  Optional[Any]=None					)   ->Optional[Any]:
            super().__init__(_snake_case					)

            snake_case__							:	Union[str, Any]   	=	speaker_embeddings






     @classmethod
     def    lowercase_   (       cls  :  Optional[int], _snake_case  :  Optional[int], _snake_case  :  Dict="speaker_embeddings_path.json", **_snake_case  :  Optional[Any]					)   ->Dict:

            if speaker_embeddings_dict_path is not None:
                   snake_case__							:	Any   	=	get_file_from_repo(
                       _snake_case, _snake_case, subfolder=kwargs.pop('subfolder', _snake_case					), cache_dir=kwargs.pop('cache_dir', _snake_case					), force_download=kwargs.pop('force_download', _snake_case					), proxies=kwargs.pop('proxies', _snake_case					), resume_download=kwargs.pop('resume_download', _snake_case					), local_files_only=kwargs.pop('local_files_only', _snake_case					), use_auth_token=kwargs.pop('use_auth_token', _snake_case					), revision=kwargs.pop('revision', _snake_case					), )
                   if speaker_embeddings_path is None:
                          logger.warning(
                              F'''`{os.path.join(_snake_case, _snake_case					)}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'''					)
                          snake_case__							:	Union[str, Any]   	=	None
                   else:
                          with open(_snake_case					) as speaker_embeddings_json:
                                 snake_case__							:	Union[str, Any]   	=	json.load(_snake_case					)
            else:
                   snake_case__							:	Optional[Any]   	=	None

            snake_case__							:	Optional[int]   	=	AutoTokenizer.from_pretrained(_snake_case, **_snake_case					)

            return cls(tokenizer=_snake_case, speaker_embeddings=_snake_case					)






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  Tuple, _snake_case  :  Tuple="speaker_embeddings_path.json", _snake_case  :  Dict="speaker_embeddings", _snake_case  :  bool = False, **_snake_case  :  int, )   ->int:
            if self.speaker_embeddings is not None:
                   os.makedirs(os.path.join(_snake_case, _snake_case, 'v2'					), exist_ok=_snake_case					)

                   snake_case__							:	Optional[Any]   	=	{}

                   snake_case__							:	Any   	=	save_directory

                   for prompt_key in self.speaker_embeddings:
                          if prompt_key != "repo_or_path":
                                 snake_case__							:	Union[str, Any]   	=	self._load_voice_preset(_snake_case					)

                                 snake_case__							:	List[str]   	=	{}
                                 for key in self.speaker_embeddings[prompt_key]:
                                        np.save(
                                            os.path.join(
                                                embeddings_dict['repo_or_path'], _snake_case, F'''{prompt_key}_{key}'''					), voice_preset[key], allow_pickle=_snake_case, )
                                        snake_case__							:	Dict   	=	os.path.join(_snake_case, F'''{prompt_key}_{key}.npy'''					)

                                 snake_case__							:	List[str]   	=	tmp_dict

                   with open(os.path.join(_snake_case, _snake_case					), 'w'					) as fp:
                          json.dump(_snake_case, _snake_case					)

            super().save_pretrained(_snake_case, _snake_case, **_snake_case					)






     def    lowercase_   (       self  :  int, _snake_case  :  str = None, **_snake_case  :  Dict					)   ->Tuple:
            snake_case__							:	int   	=	self.speaker_embeddings[voice_preset]

            snake_case__							:	Tuple   	=	{}
            for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
                   if key not in voice_preset_paths:
                          raise ValueError(
                              F'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'''					)

                   snake_case__							:	Dict   	=	get_file_from_repo(
                       self.speaker_embeddings.get('repo_or_path', '/'					), voice_preset_paths[key], subfolder=kwargs.pop('subfolder', _snake_case					), cache_dir=kwargs.pop('cache_dir', _snake_case					), force_download=kwargs.pop('force_download', _snake_case					), proxies=kwargs.pop('proxies', _snake_case					), resume_download=kwargs.pop('resume_download', _snake_case					), local_files_only=kwargs.pop('local_files_only', _snake_case					), use_auth_token=kwargs.pop('use_auth_token', _snake_case					), revision=kwargs.pop('revision', _snake_case					), )
                   if path is None:
                          raise ValueError(
                              F'''`{os.path.join(self.speaker_embeddings.get("repo_or_path", "/"					), voice_preset_paths[key]					)}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''					)

                   snake_case__							:	Tuple   	=	np.load(_snake_case					)

            return voice_preset_dict






     def    lowercase_   (       self  :  Optional[int], _snake_case  :  Optional[dict] = None					)   ->Any:
            for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
                   if key not in voice_preset:
                          raise ValueError(F'''Voice preset unrecognized, missing {key} as a key.'''					)

                   if not isinstance(voice_preset[key], np.ndarray					):
                          raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]					)}D ndarray.'''					)

                   if len(voice_preset[key].shape					) != self.preset_shape[key]:
                          raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]					)}D ndarray.'''					)






     def __call__(       self  :  Optional[Any], _snake_case  :  List[Any]=None, _snake_case  :  Optional[Any]=None, _snake_case  :  int="pt", _snake_case  :  Any=2_5_6, _snake_case  :  str=False, _snake_case  :  Tuple=True, _snake_case  :  Dict=False, **_snake_case  :  List[str], )   ->Tuple:
            if voice_preset is not None and not isinstance(_snake_case, _snake_case					):
                   if (
                       isinstance(_snake_case, _snake_case					)
                       and self.speaker_embeddings is not None
                       and voice_preset in self.speaker_embeddings
                   ):
                          snake_case__							:	Dict   	=	self._load_voice_preset(_snake_case					)

                   else:
                          if isinstance(_snake_case, _snake_case					) and not voice_preset.endswith('.npz'					):
                                 snake_case__							:	Union[str, Any]   	=	voice_preset + '.npz'

                          snake_case__							:	List[Any]   	=	np.load(_snake_case					)

            if voice_preset is not None:
                   self._validate_voice_preset_dict(_snake_case, **_snake_case					)
                   snake_case__							:	Union[str, Any]   	=	BatchFeature(data=_snake_case, tensor_type=_snake_case					)

            snake_case__							:	Any   	=	self.tokenizer(
                _snake_case, return_tensors=_snake_case, padding='max_length', max_length=_snake_case, return_attention_mask=_snake_case, return_token_type_ids=_snake_case, add_special_tokens=_snake_case, **_snake_case, )

            if voice_preset is not None:
                   snake_case__							:	Optional[Any]   	=	voice_preset

            return encoded_text











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        lowercase_							(A : List[str]      ):
       snake_case__							:	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(A		,					A      )
def        lowercase_							(A : str      ):
       snake_case__	,				snake_case__							:	Union[str, Any]   	=	emb.weight.shape
       snake_case__							:	str   	=	nn.Linear(A		,					A		,					bias=A      )
       snake_case__							:	str   	=	emb.weight.data
       return lin_layer
def        lowercase_							(A : Optional[int]		,					A : Union[str, Any]=None      ):
       snake_case__							:	Any   	=	{}
       for old_key in state_dict.keys():
              snake_case__							:	Tuple   	=	old_key
              if "moe_layer.experts." in key:
                     if expert_idx is not None:
                            snake_case__							:	int   	=	key.replace('moe_layer.experts.0'		,					F'''ffn.experts.expert_{expert_idx}'''      )
                     else:
                            snake_case__							:	Any   	=	key.replace('moe_layer.experts.'		,					'ffn.experts.expert_'      )
              if "gate" in key:
                     snake_case__							:	Dict   	=	key.replace('.moe_layer.gate.wg'		,					'.ffn.router.classifier'      )
              if "fc2" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc2.'		,					'.ffn.fc2.'      )
              if "fc1" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc1.'		,					'.ffn.fc1.'      )
              if ".encoder_attn." in key:
                     snake_case__							:	Tuple   	=	key.replace('.encoder_attn.'		,					'.cross_attention.'      )
              if "encoder_attn_layer_norm" in key:
                     snake_case__							:	Tuple   	=	key.replace('encoder_attn_layer_norm'		,					'cross_attention_layer_norm'      )
              if "final_layer_norm" in key:
                     snake_case__							:	Optional[int]   	=	key.replace('final_layer_norm'		,					'ff_layer_norm'      )
              snake_case__							:	Dict   	=	state_dict[old_key]
       return new_dict
def        lowercase_							(A : List[Any]		,					A : Tuple		,					A : List[Any]		,					A : List[str]		,					A : str = WEIGHTS_NAME      ):
       snake_case__							:	Dict   	=	[]
       snake_case__							:	str   	=	0
       os.makedirs(A		,					exist_ok=A      )

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

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

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

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

       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_   :Optional[Any]        =      parser.parse_args()
 a_  ,						a_   :Optional[Any]        =      shard_on_the_fly(
     args.nllb_moe_checkpoint_path,
     args.pytorch_dump_folder_path,
     128,
     args.dtype,
 )

 a_   :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)
 a_   :int        =      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print("Done")
 model.save_pretrained(args.pytorch_dump_folder_path)






