'''simple docstring'''
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 			__lowercase		(  _lowerCamelCase						):







     """simple docstring"""






     @slow
     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,			'prajjwal1/bert-tiny'    )
         __UpperCamelCase	:	Optional[int]		    =  BertTokenizer.from_pretrained('bert-base-uncased'    )

         __UpperCamelCase	:	str		    =  bertabert.config.encoder.vocab_size
         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.sep_token_id
         __UpperCamelCase	:	Any		    =  tokenizer.cls_token_id
         __UpperCamelCase	:	Optional[Any]		    =  1_2_8

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

         __UpperCamelCase	:	Dict		    =  train_dataset.select(range(3_2    )    )
         __UpperCamelCase	:	List[Any]		    =  val_dataset.select(range(1_6    )    )

         __UpperCamelCase	:	Optional[int]		    =  4

         def _map_to_encoder_decoder_inputs(_lowerCamelCase    ):
             # Tokenizer will automatically set [BOS] <text> [EOS]
             __UpperCamelCase	:	Union[str, Any]		    =  tokenizer(batch['article']     ,			padding='max_length'     ,			truncation=_lowerCamelCase     ,			max_length=5_1_2    )
             __UpperCamelCase	:	Optional[int]		    =  tokenizer(batch['highlights']     ,			padding='max_length'     ,			truncation=_lowerCamelCase     ,			max_length=1_2_8    )
             __UpperCamelCase	:	Any		    =  inputs.input_ids
             __UpperCamelCase	:	Optional[Any]		    =  inputs.attention_mask

             __UpperCamelCase	:	Optional[Any]		    =  outputs.input_ids
             __UpperCamelCase	:	List[Any]		    =  outputs.input_ids.copy()
             __UpperCamelCase	:	Optional[Any]		    =  [
                 [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
             ]
             __UpperCamelCase	:	Union[str, Any]		    =  outputs.attention_mask

             assert all(len(_lowerCamelCase    ) == 5_1_2 for x in inputs.input_ids    )
             assert all(len(_lowerCamelCase    ) == 1_2_8 for x in outputs.input_ids    )

             return batch

         def _compute_metrics(_lowerCamelCase    ):
             __UpperCamelCase	:	str		    =  pred.label_ids
             __UpperCamelCase	:	Dict		    =  pred.predictions

             # all unnecessary tokens are removed
             __UpperCamelCase	:	Tuple		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )
             __UpperCamelCase	:	Optional[Any]		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )

             __UpperCamelCase	:	Dict		    =  sum([int(pred_str[i] == label_str[i]    ) for i in range(len(_lowerCamelCase    )    )]    ) / len(_lowerCamelCase    )

             return {"accuracy": accuracy}

         # map train dataset
         __UpperCamelCase	:	str		    =  train_dataset.map(
             _map_to_encoder_decoder_inputs     ,			batched=_lowerCamelCase     ,			batch_size=_lowerCamelCase     ,			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	:	Any		    =  val_dataset.map(
             _map_to_encoder_decoder_inputs     ,			batched=_lowerCamelCase     ,			batch_size=_lowerCamelCase     ,			remove_columns=['article', 'highlights']     ,			)
         val_dataset.set_format(
             type='torch'     ,			columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,			)

         __UpperCamelCase	:	Optional[Any]		    =  self.get_auto_remove_tmp_dir()

         __UpperCamelCase	:	List[Any]		    =  SeqaSeqTrainingArguments(
             output_dir=_lowerCamelCase     ,			per_device_train_batch_size=_lowerCamelCase     ,			per_device_eval_batch_size=_lowerCamelCase     ,			predict_with_generate=_lowerCamelCase     ,			evaluation_strategy='steps'     ,			do_train=_lowerCamelCase     ,			do_eval=_lowerCamelCase     ,			warmup_steps=0     ,			eval_steps=2     ,			logging_steps=2     ,			)

         # instantiate trainer
         __UpperCamelCase	:	str		    =  SeqaSeqTrainer(
             model=_lowerCamelCase     ,			args=_lowerCamelCase     ,			compute_metrics=_compute_metrics     ,			train_dataset=_lowerCamelCase     ,			eval_dataset=_lowerCamelCase     ,			tokenizer=_lowerCamelCase     ,			)

         # start training
         trainer.train()




'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


a=  logging.get_logger(__name__)

a=  '''▁'''

a=  {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

a=  {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

a=  {
    '''facebook/s2t-small-librispeech-asr''': 1_0_2_4,
}

a=  ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

a=  {'''mustc''': MUSTC_LANGS}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__		   =						MAX_MODEL_INPUT_SIZES
     SCREAMING_SNAKE_CASE__		   =						['''input_ids''', '''attention_mask''']

     SCREAMING_SNAKE_CASE__		   =						[]







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="<pad>"     ,			_lowerCamelCase="<unk>"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	List[Any]		    =  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_lowerCamelCase     ,			eos_token=_lowerCamelCase     ,			unk_token=_lowerCamelCase     ,			pad_token=_lowerCamelCase     ,			do_upper_case=_lowerCamelCase     ,			do_lower_case=_lowerCamelCase     ,			tgt_lang=_lowerCamelCase     ,			lang_codes=_lowerCamelCase     ,			sp_model_kwargs=self.sp_model_kwargs     ,			**_lowerCamelCase     ,			)
         __UpperCamelCase	:	Union[str, Any]		    =  do_upper_case
         __UpperCamelCase	:	Dict		    =  do_lower_case

         __UpperCamelCase	:	List[str]		    =  load_json(_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  {v: k for k, v in self.encoder.items()}
         __UpperCamelCase	:	int		    =  spm_file
         __UpperCamelCase	:	List[Any]		    =  load_spm(_lowerCamelCase     ,			self.sp_model_kwargs    )

         if lang_codes is not None:
             __UpperCamelCase	:	Any		    =  lang_codes
             __UpperCamelCase	:	Any		    =  LANGUAGES[lang_codes]
             __UpperCamelCase	:	str		    =  [f"""<lang:{lang}>""" for lang in self.langs]
             __UpperCamelCase	:	List[str]		    =  {lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""    ) for lang in self.langs}

             __UpperCamelCase	:	str		    =  self.lang_tokens
             __UpperCamelCase	:	str		    =  tgt_lang if tgt_lang is not None else self.langs[0]

             self.set_tgt_lang_special_tokens(self._tgt_lang    )
         else:
             __UpperCamelCase	:	Dict		    =  {}







     @property
     def lowerCAmelCase     (		self    ):
         return len(self.encoder    )







     @property
     def lowerCAmelCase     (		self    ):
         return self._tgt_lang







     @tgt_lang.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[int]		    =  new_tgt_lang
         self.set_tgt_lang_special_tokens(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  self.lang_code_to_id[tgt_lang]
         __UpperCamelCase	:	List[str]		    =  [lang_code_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.sp_model.encode(_lowerCamelCase     ,			out_type=_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.encoder.get(_lowerCamelCase     ,			self.encoder[self.unk_token]    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.decoder.get(_lowerCamelCase     ,			self.unk_token    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  []
         __UpperCamelCase	:	List[str]		    =  ''
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 __UpperCamelCase	:	int		    =  self.sp_model.decode(_lowerCamelCase    )
                 out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                 __UpperCamelCase	:	int		    =  []
             else:
                 current_sub_tokens.append(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  self.sp_model.decode(_lowerCamelCase    )
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase=None    ):
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = False    ):

         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_lowerCamelCase     ,			token_ids_a=_lowerCamelCase     ,			already_has_special_tokens=_lowerCamelCase    )

         __UpperCamelCase	:	Union[str, Any]		    =  [1] * len(self.prefix_tokens    )
         __UpperCamelCase	:	Any		    =  [1]
         if token_ids_a is None:
             return prefix_ones + ([0] * len(_lowerCamelCase    )) + suffix_ones
         return prefix_ones + ([0] * len(_lowerCamelCase    )) + ([0] * len(_lowerCamelCase    )) + suffix_ones







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder.copy()
         vocab.update(self.added_tokens_encoder    )
         return vocab







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







     def __setstate__(		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  d

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

         __UpperCamelCase	:	Union[str, Any]		    =  load_spm(self.spm_file     ,			self.sp_model_kwargs    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  Path(_lowerCamelCase    )
         assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
         __UpperCamelCase	:	Optional[int]		    =  save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
         )
         __UpperCamelCase	:	Union[str, Any]		    =  save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
         )

         save_json(self.encoder     ,			_lowerCamelCase    )

         if os.path.abspath(self.spm_file    ) != os.path.abspath(_lowerCamelCase    ) and os.path.isfile(self.spm_file    ):
             copyfile(self.spm_file     ,			_lowerCamelCase    )
         elif not os.path.isfile(self.spm_file    ):
             with open(_lowerCamelCase     ,			'wb'    ) as fi:
                 __UpperCamelCase	:	Dict		    =  self.sp_model.serialized_model_proto()
                 fi.write(_lowerCamelCase    )

         return (str(_lowerCamelCase    ), str(_lowerCamelCase    ))







def  _UpperCamelCase (     _a	:	str				,     _a	:	Dict[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  sentencepiece.SentencePieceProcessor(**_a			)
    spm.Load(str(_a			)			)
    return spm







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    with open(_a				,     'r'			) as f:
        return json.load(_a			)







def  _UpperCamelCase (     _a	:	Any				,     _a	:	str			):




    """simple docstring"""



    with open(_a				,     'w'			) as f:
        json.dump(_a				,     _a				,     indent=2			)



