


import os
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			=						logging.get_logger(__name__)

_A			=						{'''vocab_file''': '''sentencepiece.bpe.model'''}

_A			=						{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
}

_A			=						{
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

_A			=						'''▁'''






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]              =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		List[Any]              =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		Tuple              =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		Optional[int]              =  ["input_ids", "attention_mask"]






    def __init__(      self ,       A_ ,       A_="<s>" ,       A_="</s>" ,       A_="</s>" ,       A_="<s>" ,       A_="<unk>" ,       A_="<pad>" ,       A_="<mask>" ,       A_ = None ,       **A_ ,       )  ->	None:
         # Mask token behave like a normal word, i.e. include the space before it
         __UpperCamelCase   							=AddedToken(A_ ,       lstrip=A_ ,       rstrip=A_   ) if isinstance(A_ ,       A_   ) else mask_token

         __UpperCamelCase   							={} if sp_model_kwargs is None else sp_model_kwargs

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

         __UpperCamelCase   							=vocab_file
         __UpperCamelCase   							=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(str(A_   )   )

         __UpperCamelCase   							={'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

         __UpperCamelCase   							=len(self.sp_model   ) - 1
         __UpperCamelCase   							={v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def _a (      self ,       A_ ,       A_ = None   )  ->	List[int]:

         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __UpperCamelCase   							=[self.cls_token_id]
         __UpperCamelCase   							=[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






    def _a (      self ,       A_ ,       A_ = None ,       A_ = False   )  ->	List[int]:
         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, 1] + ([0] * len(A_   )) + [1]






    def _a (      self ,       A_ ,       A_ = None   )  ->	List[int]:
         __UpperCamelCase   							=[self.sep_token_id]
         __UpperCamelCase   							=[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 + sep + token_ids_a + sep   ) * [0]






    @property
    def _a (      self   )  ->	Any:
         return len(self.sp_model   )






    def _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							={self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
         vocab.update(self.added_tokens_encoder   )
         return vocab






    def _a (      self ,       A_   )  ->	List[str]:
         return self.sp_model.encode(A_ ,       out_type=A_   )






    def _a (      self ,       A_   )  ->	int:
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         __UpperCamelCase   							=self.sp_model.PieceToId(A_   )

         return spm_id if spm_id else self.unk_token_id






    def _a (      self ,       A_   )  ->	Dict:
         if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(A_   )






    def _a (      self ,       A_   )  ->	Dict:
         __UpperCamelCase   							=[]
         __UpperCamelCase   							=''
         __UpperCamelCase   							=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
                   __UpperCamelCase   							=True
                   __UpperCamelCase   							=[]
              else:
                   current_sub_tokens.append(A_   )
                   __UpperCamelCase   							=False
         out_string += self.sp_model.decode(A_   )
         return out_string.strip()






    def __getstate__(      self   )  ->	int:
         __UpperCamelCase   							=self.__dict__.copy()
         __UpperCamelCase   							=None
         return state






    def __setstate__(      self ,       A_   )  ->	Union[str, Any]:
         __UpperCamelCase   							=d

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

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






    def _a (      self ,       A_ ,       A_ = None   )  ->	Tuple[str]:
         if not os.path.isdir(A_   ):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
              return
         __UpperCamelCase   							=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:
                   __UpperCamelCase   							=self.sp_model.serialized_model_proto()
                   fi.write(A_   )

         return (out_vocab_file,)









import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler






@dataclass
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		int              =  0
    UpperCAmelCase__						:		bool              =  False
    UpperCAmelCase__						:		float              =  3.0






class    UpperCAmelCase__	( unittest.TestCase		):



    """simple docstring"""






    def _a (      self   )  ->	Any:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs() ,       {}   )
         self.assertDictEqual(MockClass(a=2   ).to_kwargs() ,       {'a': 2}   )
         self.assertDictEqual(MockClass(a=2 ,       b=A_   ).to_kwargs() ,       {'a': 2, 'b': True}   )
         self.assertDictEqual(MockClass(a=2 ,       c=2.25   ).to_kwargs() ,       {'a': 2, 'c': 2.25}   )






    @require_cuda
    def _a (      self   )  ->	Union[str, Any]:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         __UpperCamelCase   							=GradScalerKwargs(init_scale=1024 ,       growth_factor=2   )
         AcceleratorState._reset_state()
         __UpperCamelCase   							=Accelerator(mixed_precision='fp16' ,       kwargs_handlers=[scaler_handler]   )
         print(accelerator.use_fpaa   )
         __UpperCamelCase   							=accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale ,       1024.0   )
         self.assertEqual(scaler._growth_factor ,       2.0   )

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor ,       0.5   )
         self.assertEqual(scaler._growth_interval ,       2000   )
         self.assertEqual(scaler._enabled ,       A_   )






    @require_multi_gpu
    def _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							=['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__   )]
         execute_subprocess_async(A_ ,       env=os.environ.copy()   )


if __name__ == "__main__":
   _A			=						DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _A			=						Accelerator(kwargs_handlers=[ddp_scaler])
   _A			=						torch.nn.Linear(100, 200)
   _A			=						accelerator.prepare(model)

   # Check the values changed in kwargs
   _A			=						''
   _A			=						model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)





