"""simple docstring"""


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


UpperCAmelCase__			=    logging.get_logger(__name__)

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

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

UpperCAmelCase__			=    {"""bert_for_seq_generation""": 5_1_2}


class 							a					(		SCREAMING_SNAKE_CASE_ ):
    _snake_case  :	Optional[Any]  				=				VOCAB_FILES_NAMES
    _snake_case  :	str  				=				PRETRAINED_VOCAB_FILES_MAP
    _snake_case  :	List[Any]  				=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _snake_case  :	Optional[Any]  				=				[]
    _snake_case  :	Optional[int]  				=				['input_ids', 'attention_mask']


    def __init__(       self						:	List[str] ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	int="<s>" ,					__lowerCAmelCase						:	List[Any]="</s>" ,					__lowerCAmelCase						:	Any="<unk>" ,					__lowerCAmelCase						:	Optional[Any]="<pad>" ,					__lowerCAmelCase						:	List[Any]="<::::>" ,					__lowerCAmelCase						:	Optional[Dict[str, Any]] = None ,					**__lowerCAmelCase						:	Dict ,					):
      _UpperCAmelCase    			=   {} if sp_model_kwargs is None else sp_model_kwargs

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

      _UpperCAmelCase    			=   vocab_file

      _UpperCAmelCase    			=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(__SCREAMING_SNAKE_CASE       )


    @property
    def   lowerCAmelCase_	(       self						:	Tuple       ):
      return self.sp_model.get_piece_size()


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab


    def __getstate__(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.__dict__.copy()
      _UpperCAmelCase    			=   None
      return state


    def __setstate__(       self						:	Tuple ,					__lowerCAmelCase						:	List[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   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	str       ):
      return self.sp_model.encode(__SCREAMING_SNAKE_CASE ,					out_type=__SCREAMING_SNAKE_CASE       )


    def   lowerCAmelCase_	(       self						:	Optional[Any] ,					__lowerCAmelCase						:	Optional[Any]       ):
      return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE       )


    def   lowerCAmelCase_	(       self						:	List[Any] ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE       )
      return token


    def   lowerCAmelCase_	(       self						:	Any ,					__lowerCAmelCase						:	str       ):
      _UpperCAmelCase    			=   []
      _UpperCAmelCase    			=   ''''''
      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(__SCREAMING_SNAKE_CASE       ) + token
          _UpperCAmelCase    			=   []
        else:
          current_sub_tokens.append(__SCREAMING_SNAKE_CASE       )
      out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE       )
      return out_string.strip()


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Optional[str] = None       ):
      if not os.path.isdir(__SCREAMING_SNAKE_CASE       ):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
        return
      _UpperCAmelCase    			=   os.path.join(
          __SCREAMING_SNAKE_CASE ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

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

      return (out_vocab_file,)








'''simple docstring'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__lowerCAmelCase									=   'facebook/wmt19-en-de'
__lowerCAmelCase									=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase									=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__lowerCAmelCase									=   FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
__lowerCAmelCase									=   tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCAmelCase									=   tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
__lowerCAmelCase									=   'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-de




