


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


__snake_case   							=						'''▁'''

__snake_case   							=						{'''vocab_file''': '''spiece.model'''}

__snake_case   							=						{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

__snake_case   							=						{
    '''google/pegasus-xsum''': 5_1_2,
}


__snake_case   							=						logging.get_logger(__name__)



class 	__lowerCamelCase			(_a						):
       _lowercase											=     VOCAB_FILES_NAMES

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







       def __init__(			self: List[Any],A_: Dict,A_: Optional[int]="<pad>",A_: Tuple="</s>",A_: int="<unk>",A_: Tuple="<mask_2>",A_: Optional[int]="<mask_1>",A_: str=None,A_: List[Any]=103,A_: Optional[Dict[str, Any]] = None,**A_: Dict,):






            '''simple docstring'''




            __UpperCamelCase =			offset
            if additional_special_tokens is not None:
                 if not isinstance(A_,A_					):
                      raise TypeError(
                          F'''additional_special_tokens should be of type {type(A_					)}, but is'''
                          F''' {type(A_					)}'''					)

                 __UpperCamelCase =			(
                     ([mask_token_sent] + additional_special_tokens)
                     if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                     else additional_special_tokens
                 )
                 # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                 additional_special_tokens_extended += [
                     F'''<unk_{i}>''' for i in range(len(A_					),self.offset - 1					)
                 ]

                 if len(set(A_					)					) != len(A_					):
                      raise ValueError(
                          'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
                          F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''					)
                 __UpperCamelCase =			additional_special_tokens_extended
            else:
                 __UpperCamelCase =			[mask_token_sent] if mask_token_sent is not None else []
                 additional_special_tokens += [F'''<unk_{i}>''' for i in range(2,self.offset					)]

            __UpperCamelCase =			{} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                eos_token=A_,unk_token=A_,mask_token=A_,pad_token=A_,mask_token_sent=A_,offset=A_,additional_special_tokens=A_,sp_model_kwargs=self.sp_model_kwargs,**A_,)
            __UpperCamelCase =			mask_token_sent
            __UpperCamelCase =			vocab_file
            __UpperCamelCase =			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(A_					)

            # add special tokens to encoder dict
            __UpperCamelCase =			{
                0: self.pad_token,
                1: self.eos_token,
            }

            if self.mask_token_sent is not None:
                 self.encoder.update(
                     {
                         2: self.mask_token_sent,
                         3: self.mask_token,
                     }					)

            if self.offset > 0:
                 # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
                 # mask_token_sent is already added to list -> so start at 1
                 self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1,self.offset - 1					)}					)

            __UpperCamelCase =			{v: k for k, v in self.encoder.items()}







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            return len(self.sp_model					) + self.offset







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			{self.convert_ids_to_tokens(A_					): i for i in range(self.vocab_size					)}
            vocab.update(self.added_tokens_encoder					)
            return vocab







       def __getstate__(			self: Optional[Any]					):






            '''simple docstring'''




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







       def __setstate__(			self: Dict,A_: Optional[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 		snake_case_	(			self: Optional[int],A_: str					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: str,A_: str					):






            '''simple docstring'''




            if token in self.decoder:
                 return self.decoder[token]
            elif token in self.added_tokens_decoder:
                 return self.added_tokens_decoder[token]
            __UpperCamelCase =			self.sp_model.piece_to_id(A_					)
            return sp_id + self.offset







       def 		snake_case_	(			self: Union[str, Any],A_: int					):






            '''simple docstring'''




            if index in self.encoder:
                 return self.encoder[index]
            elif index in self.added_tokens_encoder:
                 return self.added_tokens_encoder[index]
            else:
                 __UpperCamelCase =			self.sp_model.IdToPiece(index - self.offset					)
            return token







       def 		snake_case_	(			self: Union[str, Any],A_: List[Any]					):






            '''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(A_					) + token
                      __UpperCamelCase =			[]
                 else:
                      current_sub_tokens.append(A_					)
            out_string += self.sp_model.decode(A_					)
            return out_string.strip()







       def 		snake_case_	(			self: Dict,A_: Any=False					):






            '''simple docstring'''




            return 1







       def 		snake_case_	(			self: Union[str, Any],A_: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			set(self.all_special_ids					)  # call it once instead of inside list comp
            all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

            return [1 if x in all_special_ids else 0 for x in seq]







       def 		snake_case_	(			self: Optional[Any],A_: List,A_: Optional[List] = None,A_: bool = False					):






            '''simple docstring'''




            if already_has_special_tokens:
                 return self._special_token_mask(A_					)
            elif token_ids_a is None:
                 return self._special_token_mask(A_					) + [1]
            else:
                 return self._special_token_mask(token_ids_a + token_ids_a					) + [1]







       def 		snake_case_	(			self: Optional[Any],A_: Optional[int],A_: Tuple=None					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: List[str],A_: str,A_: Optional[str] = None					):






            '''simple docstring'''




            if not os.path.isdir(A_					):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
                 return
            __UpperCamelCase =			os.path.join(
                A_,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

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

            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 	__lowerCamelCase			(_a						):







       @slow
       @require_torch
       def 		snake_case_	(			self: Union[str, Any]					):






            '''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(A_: Dict					):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 __UpperCamelCase =			tokenizer(batch['article'],padding='max_length',truncation=A_,max_length=512					)
                 __UpperCamelCase =			tokenizer(batch['highlights'],padding='max_length',truncation=A_,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(A_					) == 512 for x in inputs.input_ids					)
                 assert all(len(A_					) == 128 for x in outputs.input_ids					)

                 return batch

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

                 # all unnecessary tokens are removed
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)

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

                 return {"accuracy": accuracy}

            # map train dataset
            __UpperCamelCase =			train_dataset.map(
                _map_to_encoder_decoder_inputs,batched=A_,batch_size=A_,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=A_,batch_size=A_,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=A_,per_device_train_batch_size=A_,per_device_eval_batch_size=A_,predict_with_generate=A_,evaluation_strategy='steps',do_train=A_,do_eval=A_,warmup_steps=0,eval_steps=2,logging_steps=2,)

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

            # start training
            trainer.train()


