


'''simple docstring'''






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

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


if is_sentencepiece_available():
							from .tokenization_pegasus import PegasusTokenizer
else:
							__A						:						List[Any]  =  None


__A						:						Optional[Any]  =  logging.get_logger(__name__)


__A						:						str  =  '▁'

__A						:						Optional[Any]  =  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__A						:						Any  =  {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

__A						:						Optional[int]  =  {
    'google/pegasus-xsum': 512,
}
class     __UpperCamelCase						(    UpperCAmelCase__				):
							lowercase      :		Dict 		=	VOCAB_FILES_NAMES
							lowercase      :		List[Any] 		=	PRETRAINED_VOCAB_FILES_MAP
							lowercase      :		Optional[int] 		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase      :		Tuple 		=	PegasusTokenizer
							lowercase      :		List[str] 		=	['input_ids', 'attention_mask']





							def __init__(	self		:Tuple	,_UpperCamelCase		:Union[str, Any]=None	,_UpperCamelCase		:List[Any]=None	,_UpperCamelCase		:Union[str, Any]="<pad>"	,_UpperCamelCase		:int="</s>"	,_UpperCamelCase		:int="<unk>"	,_UpperCamelCase		:Optional[Any]="<mask_2>"	,_UpperCamelCase		:Optional[Any]="<mask_1>"	,_UpperCamelCase		:Optional[Any]=None	,_UpperCamelCase		:Dict=1_0_3	,**_UpperCamelCase		:Dict	,):
									snake_case_		:				List[str]      							=    offset

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

											snake_case_		:				List[Any]      							=    (
											    ([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(_UpperCAmelCase      )	,self.offset - 1      )
											]

											if len(set(_UpperCAmelCase      )      ) != len(_UpperCAmelCase      ):
													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}.'''      )
											snake_case_		:				List[str]      							=    additional_special_tokens_extended
									else:
											snake_case_		:				Any      							=    [mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [F'''<unk_{i}>''' for i in range(2	,self.offset      )]

									super().__init__(
									    _UpperCAmelCase	,tokenizer_file=_UpperCAmelCase	,pad_token=_UpperCAmelCase	,eos_token=_UpperCAmelCase	,unk_token=_UpperCAmelCase	,mask_token=_UpperCAmelCase	,mask_token_sent=_UpperCAmelCase	,offset=_UpperCAmelCase	,additional_special_tokens=_UpperCAmelCase	,**_UpperCAmelCase	,)
									snake_case_		:				Dict      							=    vocab_file
									snake_case_		:				str      							=    False if not self.vocab_file else True





							def 		a__ (	self		:Optional[Any]	,_UpperCamelCase		:Tuple      ):
									snake_case_		:				Any      							=    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

									if all_special_ids != set(range(len(self.additional_special_tokens      ) + 3      )      ):
											raise ValueError(
											    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
											    F''' {len(self.additional_special_tokens      )} additional_special_tokens, but got {all_special_ids}'''      )

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





							def 		a__ (	self		:Optional[Any]	,_UpperCamelCase		:List	,_UpperCamelCase		:Optional[List] = None	,_UpperCamelCase		:bool = False      ):


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





							def 		a__ (	self		:int	,_UpperCamelCase		:Optional[int]	,_UpperCamelCase		:Union[str, Any]=None      ):


									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 		a__ (	self		:str	,_UpperCamelCase		:str	,_UpperCamelCase		:Optional[str] = None      ):


									if not self.can_save_slow_tokenizer:
											raise ValueError(
											    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
											    """tokenizer."""      )

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

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

									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__				):
					'''simple docstring'''






					@slow
					@require_torch
					def     lowerCamelCase__	(self       :		int						)		->  Tuple:




							"""simple docstring"""


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

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

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

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

							lowercase__	     =						4

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

									lowercase__	     =						outputs.input_ids
									lowercase__	     =						outputs.input_ids.copy()
									lowercase__	     =						[
									    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
									]
									lowercase__	     =						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       :		List[Any]						):
									lowercase__	     =						pred.label_ids
									lowercase__	     =						pred.predictions

									# all unnecessary tokens are removed
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)

									lowercase__	     =						sum([int(pred_str[i] == label_str[i]						) for i in range(len(_UpperCAmelCase						)						)]						) / len(_UpperCAmelCase						)

									return {"accuracy": accuracy}

							# map train dataset
							lowercase__	     =						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
							lowercase__	     =						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"""] ,   )

							lowercase__	     =						self.get_auto_remove_tmp_dir()

							lowercase__	     =						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
							lowercase__	     =						SeqaSeqTrainer(
							    model=_UpperCAmelCase ,   args=_UpperCAmelCase ,   compute_metrics=_compute_metrics ,   train_dataset=_UpperCAmelCase ,   eval_dataset=_UpperCAmelCase ,   tokenizer=_UpperCAmelCase ,   )

							# start training
							trainer.train()

