




'''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 ):







   @slow
   @require_torch
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'      ,							'prajjwal1/bert-tiny'      )
     __a			=					BertTokenizer.from_pretrained('bert-base-uncased'      )

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

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

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

     __a			=					4

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

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

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

       return batch

     def _compute_metrics(UpperCamelCase      ):
       __a			=					pred.label_ids
       __a			=					pred.predictions

       # all unnecessary tokens are removed
       __a			=					tokenizer.batch_decode(UpperCamelCase_      ,							skip_special_tokens=UpperCamelCase_      )
       __a			=					tokenizer.batch_decode(UpperCamelCase_      ,							skip_special_tokens=UpperCamelCase_      )

       __a			=					sum([int(pred_str[i] == label_str[i]      ) for i in range(len(UpperCamelCase_      )      )]      ) / len(UpperCamelCase_      )

       return {"accuracy": accuracy}

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

     __a			=					self.get_auto_remove_tmp_dir()

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

     # instantiate trainer
     __a			=					SeqaSeqTrainer(
         model=UpperCamelCase_      ,							args=UpperCamelCase_      ,							compute_metrics=_compute_metrics      ,							train_dataset=UpperCamelCase_      ,							eval_dataset=UpperCamelCase_      ,							tokenizer=UpperCamelCase_      ,							)

     # start training
     trainer.train()






'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_ 		=      {
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

UpperCAmelCase_ 		=      {
    "junnyu/roformer_chinese_small": 15_36,
    "junnyu/roformer_chinese_base": 15_36,
    "junnyu/roformer_chinese_char_small": 5_12,
    "junnyu/roformer_chinese_char_base": 5_12,
    "junnyu/roformer_small_discriminator": 1_28,
    "junnyu/roformer_small_generator": 1_28,
}


UpperCAmelCase_ 		=      {
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}





class    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      PRETRAINED_INIT_CONFIGURATION
   _a							=      RoFormerTokenizer







   def __init__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=True      ,							UpperCamelCase="[UNK]"      ,							UpperCamelCase="[SEP]"      ,							UpperCamelCase="[PAD]"      ,							UpperCamelCase="[CLS]"      ,							UpperCamelCase="[MASK]"      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							**UpperCamelCase      ,							)				->    int:
     super().__init__(
         UpperCamelCase      ,							tokenizer_file=UpperCamelCase      ,							do_lower_case=UpperCamelCase      ,							unk_token=UpperCamelCase      ,							sep_token=UpperCamelCase      ,							pad_token=UpperCamelCase      ,							cls_token=UpperCamelCase      ,							mask_token=UpperCamelCase      ,							tokenize_chinese_chars=UpperCamelCase      ,							strip_accents=UpperCamelCase      ,							**UpperCamelCase      ,							)

     __a			=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
     if (
         pre_tok_state.get('lowercase'      ,							UpperCamelCase      ) != do_lower_case
         or pre_tok_state.get('strip_accents'      ,							UpperCamelCase      ) != strip_accents
     ):
       __a			=					getattr(UpperCamelCase      ,							pre_tok_state.pop('type'      )      )
       __a			=					do_lower_case
       __a			=					strip_accents
       __a			=					pre_tok_class(**UpperCamelCase      )

     __a			=					do_lower_case







   def __getstate__(	self      )				->    List[Any]:
     __a			=					self.__dict__.copy()
     __a			=					BertPreTokenizer()
     return state







   def __setstate__(	self      ,							UpperCamelCase      )				->    Optional[int]:
     __a			=					d
     __a			=					self.__dict__['_tokenizer'].get_vocab()
     __a			=					PreTokenizer.custom(JiebaPreTokenizer(UpperCamelCase      )      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    int:
     __a			=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:
     __a			=					self._tokenizer.model.save(UpperCamelCase      ,							name=UpperCamelCase      )
     return tuple(UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=False      ,							**UpperCamelCase      ,							)				->    List[Any]:
     __a			=					BertPreTokenizer()
     return super().save_pretrained(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )



