






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


__magic_name__   :  Any        =							logging.get_logger(__name__)


__magic_name__   :  List[Any]        =							'''▁'''

__magic_name__   :  Optional[int]        =							{'''vocab_file''': '''spiece.model'''}

__magic_name__   :  Optional[int]        =							{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

__magic_name__   :  Optional[Any]        =							{
    '''google/reformer-crime-and-punishment''': 52_4288,
}





class 					A__     (     __snake_case ):

     '''simple docstring'''






     snake_case__					=					VOCAB_FILES_NAMES
     snake_case__					=					PRETRAINED_VOCAB_FILES_MAP
     snake_case__					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case__					=					["""input_ids""", """attention_mask"""]







     def __init__( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    List[Any]="</s>"   ,						_SCREAMING_SNAKE_CASE      :    List[str]="<unk>"   ,						_SCREAMING_SNAKE_CASE      :    List[str]=[]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Dict[str, Any]] = None   ,						**_SCREAMING_SNAKE_CASE      :    str   ,						):






           """simple docstring"""




           UpperCamelCase					       =  {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=_SCREAMING_SNAKE_CASE   ,						unk_token=_SCREAMING_SNAKE_CASE   ,						additional_special_tokens=_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   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           return self.sp_model.get_piece_size()







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           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      :    List[str] ):






           """simple docstring"""




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







     def __setstate__( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    int ):






           """simple docstring"""




           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   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Any ):






           """simple docstring"""




           return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    List[str] ):






           """simple docstring"""




           if index < self.sp_model.get_piece_size():
                 UpperCamelCase					       =  self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE )
           return token







     def   _SCREAMING_SNAKE_CASE     ( self      :    int   ,						_SCREAMING_SNAKE_CASE      :    Dict ):






           """simple docstring"""




           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   _SCREAMING_SNAKE_CASE     ( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    Optional[str] = None ):






           """simple docstring"""




           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,)











from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
       import datasets





class 					A__     (     __snake_case ):

     '''simple docstring'''






     @slow
     @require_torch
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'   ,						'prajjwal1/bert-tiny' )
           UpperCamelCase					       =  BertTokenizer.from_pretrained('bert-base-uncased' )

           UpperCamelCase					       =  bertabert.config.encoder.vocab_size
           UpperCamelCase					       =  tokenizer.sep_token_id
           UpperCamelCase					       =  tokenizer.cls_token_id
           UpperCamelCase					       =  128

           UpperCamelCase					       =  datasets.load_dataset('cnn_dailymail'   ,						'3.0.0'   ,						split='train[:1%]' )
           UpperCamelCase					       =  datasets.load_dataset('cnn_dailymail'   ,						'3.0.0'   ,						split='validation[:1%]' )

           UpperCamelCase					       =  train_dataset.select(range(32 ) )
           UpperCamelCase					       =  val_dataset.select(range(16 ) )

           UpperCamelCase					       =  4

           def _map_to_encoder_decoder_inputs(_SCREAMING_SNAKE_CASE      :    Tuple ):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 UpperCamelCase					       =  tokenizer(batch['article']   ,						padding='max_length'   ,						truncation=_SCREAMING_SNAKE_CASE   ,						max_length=512 )
                 UpperCamelCase					       =  tokenizer(batch['highlights']   ,						padding='max_length'   ,						truncation=_SCREAMING_SNAKE_CASE   ,						max_length=128 )
                 UpperCamelCase					       =  inputs.input_ids
                 UpperCamelCase					       =  inputs.attention_mask

                 UpperCamelCase					       =  outputs.input_ids
                 UpperCamelCase					       =  outputs.input_ids.copy()
                 UpperCamelCase					       =  [
                     [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                 ]
                 UpperCamelCase					       =  outputs.attention_mask

                 assert all(len(_SCREAMING_SNAKE_CASE ) == 512 for x in inputs.input_ids )
                 assert all(len(_SCREAMING_SNAKE_CASE ) == 128 for x in outputs.input_ids )

                 return batch

           def _compute_metrics(_SCREAMING_SNAKE_CASE      :    str ):
                 UpperCamelCase					       =  pred.label_ids
                 UpperCamelCase					       =  pred.predictions

                 # all unnecessary tokens are removed
                 UpperCamelCase					       =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  sum([int(pred_str[i] == label_str[i] ) for i in range(len(_SCREAMING_SNAKE_CASE ) )] ) / len(_SCREAMING_SNAKE_CASE )

                 return {"accuracy": accuracy}

           # map train dataset
           UpperCamelCase					       =  train_dataset.map(
               _map_to_encoder_decoder_inputs   ,						batched=_SCREAMING_SNAKE_CASE   ,						batch_size=_SCREAMING_SNAKE_CASE   ,						remove_columns=['article', 'highlights']   ,						)
           train_dataset.set_format(
               type='torch'   ,						columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,						)

           # same for validation dataset
           UpperCamelCase					       =  val_dataset.map(
               _map_to_encoder_decoder_inputs   ,						batched=_SCREAMING_SNAKE_CASE   ,						batch_size=_SCREAMING_SNAKE_CASE   ,						remove_columns=['article', 'highlights']   ,						)
           val_dataset.set_format(
               type='torch'   ,						columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,						)

           UpperCamelCase					       =  self.get_auto_remove_tmp_dir()

           UpperCamelCase					       =  SeqaSeqTrainingArguments(
               output_dir=_SCREAMING_SNAKE_CASE   ,						per_device_train_batch_size=_SCREAMING_SNAKE_CASE   ,						per_device_eval_batch_size=_SCREAMING_SNAKE_CASE   ,						predict_with_generate=_SCREAMING_SNAKE_CASE   ,						evaluation_strategy='steps'   ,						do_train=_SCREAMING_SNAKE_CASE   ,						do_eval=_SCREAMING_SNAKE_CASE   ,						warmup_steps=0   ,						eval_steps=2   ,						logging_steps=2   ,						)

           # instantiate trainer
           UpperCamelCase					       =  SeqaSeqTrainer(
               model=_SCREAMING_SNAKE_CASE   ,						args=_SCREAMING_SNAKE_CASE   ,						compute_metrics=_compute_metrics   ,						train_dataset=_SCREAMING_SNAKE_CASE   ,						eval_dataset=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						)

           # start training
           trainer.train()



