


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_rembert import RemBertTokenizer
else:
 lowerCamelCase		   =			None

lowerCamelCase		   =			logging.get_logger(__name__)
lowerCamelCase		   =			{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase		   =			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

lowerCamelCase		   =			{
    'google/rembert': 2_56,
}

lowerCamelCase		   =			'▁'




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    List[Any] =VOCAB_FILES_NAMES
      UpperCamelCase__				:    List[Any] =PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__				:    Dict =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__				:    Union[str, Any] =RemBertTokenizer






      def __init__(   self		:	int     ,  lowercase_		:	List[Any]=None     ,  lowercase_		:	int=None     ,  lowercase_		:	Union[str, Any]=True     ,  lowercase_		:	Optional[Any]=True     ,  lowercase_		:	Optional[int]=False     ,  lowercase_		:	Optional[Any]="[CLS]"     ,  lowercase_		:	str="[SEP]"     ,  lowercase_		:	List[Any]="<unk>"     ,  lowercase_		:	int="[SEP]"     ,  lowercase_		:	Union[str, Any]="<pad>"     ,  lowercase_		:	Tuple="[CLS]"     ,  lowercase_		:	List[str]="[MASK]"     ,  **lowercase_		:	List[str]     ,  )   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =AddedToken(lowercase_     ,  lstrip=lowercase_     ,  rstrip=lowercase_					) if isinstance(lowercase_     ,  lowercase_					) else mask_token

             super().__init__(
                 lowercase_     ,  tokenizer_file=lowercase_     ,  do_lower_case=lowercase_     ,  remove_space=lowercase_     ,  keep_accents=lowercase_     ,  bos_token=lowercase_     ,  eos_token=lowercase_     ,  unk_token=lowercase_     ,  sep_token=lowercase_     ,  pad_token=lowercase_     ,  cls_token=lowercase_     ,  mask_token=lowercase_     ,  **lowercase_     ,  )

             _lowerCamelCase			:						Union[str, Any]            =do_lower_case
             _lowerCamelCase			:						Dict            =remove_space
             _lowerCamelCase			:						List[Any]            =keep_accents
             _lowerCamelCase			:						List[str]            =vocab_file
             _lowerCamelCase			:						Optional[int]            =False if not self.vocab_file else True






      def 			lowerCamelCase			(   self		:	List[Any]     ,  lowercase_		:	List[int]     ,  lowercase_		:	Optional[List[int]] = None					)   ->		List[int]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =[self.sep_token_id]
             _lowerCamelCase			:						str            =[self.cls_token_id]
             if token_ids_a is None:
                    return cls + token_ids_a + sep
             return cls + token_ids_a + sep + token_ids_a + sep






      def 			lowerCamelCase			(   self		:	List[Any]     ,  lowercase_		:	List[int]     ,  lowercase_		:	Optional[List[int]] = None     ,  lowercase_		:	bool = False					)   ->		List[int]:
             """simple docstring"""




             if already_has_special_tokens:
                    if token_ids_a is not None:
                           raise ValueError(
                               'You should not supply a second sequence if the provided sequence of '
                               'ids is already formatted with special tokens for the model.'					)
                    return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

             if token_ids_a is not None:
                    return [1] + ([0] * len(lowercase_					)) + [1] + ([0] * len(lowercase_					)) + [1]
             return [1] + ([0] * len(lowercase_					)) + [1]






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	List[int]     ,  lowercase_		:	Optional[List[int]] = None					)   ->		List[int]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =[self.sep_token_id]
             _lowerCamelCase			:						Any            =[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					) * [0] + len(token_ids_a + sep					) * [1]






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  lowercase_		:	str     ,  lowercase_		:	Optional[str] = None					)   ->		Tuple[str]:
             """simple docstring"""




             if not os.path.isdir(lowercase_					):
                    logger.error('Vocabulary path ({}) should be a directory'.format(lowercase_					)					)
                    return
             _lowerCamelCase			:						Tuple            =os.path.join(
                 lowercase_     ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

             if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase_					):
                    copyfile(self.vocab_file     ,  lowercase_					)

             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    (  UpperCamelCase_	):






      @slow
      @require_torch
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            =EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,  'prajjwal1/bert-tiny'					)
             _lowerCamelCase			:						Dict            =BertTokenizer.from_pretrained('bert-base-uncased'					)

             _lowerCamelCase			:						Union[str, Any]            =bertabert.config.encoder.vocab_size
             _lowerCamelCase			:						Dict            =tokenizer.sep_token_id
             _lowerCamelCase			:						List[Any]            =tokenizer.cls_token_id
             _lowerCamelCase			:						Optional[int]            =128

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

             _lowerCamelCase			:						Optional[int]            =train_dataset.select(range(32					)					)
             _lowerCamelCase			:						Optional[int]            =val_dataset.select(range(16					)					)

             _lowerCamelCase			:						Optional[int]            =4

             def _map_to_encoder_decoder_inputs(lowercase_		:	Tuple					):
                    # Tokenizer will automatically set [BOS] <text> [EOS]
                    _lowerCamelCase			:						Optional[int]            =tokenizer(batch['article']     ,  padding='max_length'     ,  truncation=lowercase_     ,  max_length=512					)
                    _lowerCamelCase			:						List[str]            =tokenizer(batch['highlights']     ,  padding='max_length'     ,  truncation=lowercase_     ,  max_length=128					)
                    _lowerCamelCase			:						List[str]            =inputs.input_ids
                    _lowerCamelCase			:						Any            =inputs.attention_mask

                    _lowerCamelCase			:						List[str]            =outputs.input_ids
                    _lowerCamelCase			:						int            =outputs.input_ids.copy()
                    _lowerCamelCase			:						List[str]            =[
                        [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                    ]
                    _lowerCamelCase			:						Dict            =outputs.attention_mask

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

                    return batch

             def _compute_metrics(lowercase_		:	str					):
                    _lowerCamelCase			:						List[Any]            =pred.label_ids
                    _lowerCamelCase			:						List[Any]            =pred.predictions

                    # all unnecessary tokens are removed
                    _lowerCamelCase			:						str            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
                    _lowerCamelCase			:						Tuple            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)

                    _lowerCamelCase			:						List[str]            =sum([int(pred_str[i] == label_str[i]					) for i in range(len(lowercase_					)					)]					) / len(lowercase_					)

                    return {"accuracy": accuracy}

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

             _lowerCamelCase			:						Dict            =self.get_auto_remove_tmp_dir()

             _lowerCamelCase			:						Optional[int]            =SeqaSeqTrainingArguments(
                 output_dir=lowercase_     ,  per_device_train_batch_size=lowercase_     ,  per_device_eval_batch_size=lowercase_     ,  predict_with_generate=lowercase_     ,  evaluation_strategy='steps'     ,  do_train=lowercase_     ,  do_eval=lowercase_     ,  warmup_steps=0     ,  eval_steps=2     ,  logging_steps=2     ,  )

             # instantiate trainer
             _lowerCamelCase			:						Optional[Any]            =SeqaSeqTrainer(
                 model=lowercase_     ,  args=lowercase_     ,  compute_metrics=_compute_metrics     ,  train_dataset=lowercase_     ,  eval_dataset=lowercase_     ,  tokenizer=lowercase_     ,  )

             # start training
             trainer.train()


