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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_      =      logging.get_logger(__name__)

A_      =      {"vocab_file": "spiece.model"}

A_      =      {
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

A_      =      {"bert_for_seq_generation": 512}






class      __lowercase					(    lowercase__    ):
      lowercase					 =     VOCAB_FILES_NAMES
      lowercase					 =     PRETRAINED_VOCAB_FILES_MAP
      lowercase					 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowercase					 =     []
      lowercase					 =     ['input_ids', 'attention_mask']

      def __init__(	self				:      str							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int="<s>"							,							__lowerCamelCase				:      str="</s>"							,							__lowerCamelCase				:      Union[str, Any]="<unk>"							,							__lowerCamelCase				:      List[Any]="<pad>"							,							__lowerCamelCase				:      str="<::::>"							,							__lowerCamelCase				:      Tuple = None							,							**__lowerCamelCase				:      Dict							,							)							->		str:


         '''simple docstring'''

         lowercase							=		{} if sp_model_kwargs is None else sp_model_kwargs

         # Add extra_ids to the special token list
         super().__init__(
             bos_token=__lowerCamelCase							,							eos_token=__lowerCamelCase							,							unk_token=__lowerCamelCase							,							pad_token=__lowerCamelCase							,							sep_token=__lowerCamelCase							,							sp_model_kwargs=self.sp_model_kwargs							,							**__lowerCamelCase							,							)

         lowercase							=		vocab_file

         lowercase							=		spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(__lowerCamelCase   )

      @property
      def        __a		(	self				:      int   )							->		Union[str, Any]:


         '''simple docstring'''

         return self.sp_model.get_piece_size()

      def        __a		(	self				:      str   )							->		Union[str, Any]:


         '''simple docstring'''

         lowercase							=		{self.convert_ids_to_tokens(__lowerCamelCase   ): i for i in range(self.vocab_size   )}
         vocab.update(self.added_tokens_encoder   )
         return vocab

      def __getstate__(	self				:      Dict   )							->		List[str]:


         '''simple docstring'''

         lowercase							=		self.__dict__.copy()
         lowercase							=		None
         return state

      def __setstate__(	self				:      List[str]							,							__lowerCamelCase				:      Optional[int]   )							->		Any:


         '''simple docstring'''

         lowercase							=		d

         # for backward compatibility
         if not hasattr(self							,							'''sp_model_kwargs'''   ):
            lowercase							=		{}

         lowercase							=		spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(self.vocab_file   )

      def        __a		(	self				:      Dict							,							__lowerCamelCase				:      List[Any]   )							->		Optional[int]:


         '''simple docstring'''

         return self.sp_model.encode(__lowerCamelCase							,							out_type=__lowerCamelCase   )

      def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Optional[int]   )							->		Optional[int]:


         '''simple docstring'''

         return self.sp_model.piece_to_id(__lowerCamelCase   )

      def        __a		(	self				:      int							,							__lowerCamelCase				:      Any   )							->		List[str]:


         '''simple docstring'''

         lowercase							=		self.sp_model.IdToPiece(__lowerCamelCase   )
         return token

      def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      List[Any]   )							->		Tuple:


         '''simple docstring'''

         lowercase							=		[]
         lowercase							=		''''''
         for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
               out_string += self.sp_model.decode(__lowerCamelCase   ) + token
               lowercase							=		[]
            else:
               current_sub_tokens.append(__lowerCamelCase   )
         out_string += self.sp_model.decode(__lowerCamelCase   )
         return out_string.strip()



      def        __a		(	self				:      Any							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[int] = None   )							->		Optional[int]:


         '''simple docstring'''

         if not os.path.isdir(__lowerCamelCase   ):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
            return
         lowercase							=		os.path.join(
             __lowerCamelCase							,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

         return (out_vocab_file,)






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
A_      =      logging.get_logger(__name__)






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







			lowercase							=		torch.load(UpperCAmelCase,		map_location='''cpu'''       )
			if "model" in sd.keys():
						lowercase							=		torch.load(UpperCAmelCase,		map_location='''cpu'''       )['''model''']

			# pop unnecessary weights
			lowercase							=		[
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(UpperCAmelCase       )

			lowercase							=		{
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowercase							=		sd.pop(UpperCAmelCase       )

			lowercase							=		list(sd.keys()       )
			for key in keys:
						if ".qkv_proj." in key:
									lowercase							=		sd[key]
									# We split QKV in separate Q,K,V

									lowercase							=		key.replace('''.qkv_proj.''',		'''.q_proj.'''       )
									lowercase							=		key.replace('''.qkv_proj.''',		'''.k_proj.'''       )
									lowercase							=		key.replace('''.qkv_proj.''',		'''.v_proj.'''       )

									lowercase							=		value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowercase		,lowercase		,lowercase							=		torch.split(UpperCAmelCase,		depth // 3,		dim=0       )

									lowercase							=		q
									lowercase							=		k
									lowercase							=		v
									del sd[key]

			return sd






@torch.no_grad()
def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=None       )->       int:



			"""simple docstring"""







			lowercase							=		load_checkpoint(UpperCAmelCase       )

			if config is not None:
						lowercase							=		OPTConfig.from_pretrained(UpperCAmelCase       )
			else:
						lowercase							=		OPTConfig()

			lowercase							=		OPTModel(UpperCAmelCase       ).half().eval()
			model.load_state_dict(UpperCAmelCase       )

			# Check results
			Path(UpperCAmelCase       ).mkdir(exist_ok=UpperCAmelCase       )
			model.save_pretrained(UpperCAmelCase       )


if __name__ == "__main__":
						A_      =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--fairseq_path",
						    type=str,
						    help=(
						        "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
						        " https://huggingface.co/models?other=opt_metasq"
						    ),
						)
						parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
						A_      =      parser.parse_args()
						convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





