






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


A__      :     str       =					logging.get_logger(__name__)


A__      :     Any       =					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
A__      :     List[Any]       =					{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

A__      :     Optional[Any]       =					{
    'facebook/bart-base': 10_24,
    'facebook/bart-large': 10_24,
    'facebook/bart-large-mnli': 10_24,
    'facebook/bart-large-cnn': 10_24,
    'facebook/bart-large-xsum': 10_24,
    'yjernite/bart_eli5': 10_24,
}
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			VOCAB_FILES_NAMES
				lowercase__											=			PRETRAINED_VOCAB_FILES_MAP
				lowercase__											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__											=			["""input_ids""", """attention_mask"""]
				lowercase__											=			BartTokenizer




				def __init__(	self		:       Any,    lowerCamelCase		:       Optional[int]=None,    lowerCamelCase		:       Any=None,    lowerCamelCase		:       Optional[int]=None,    lowerCamelCase		:       Dict="replace",    lowerCamelCase		:       Dict="<s>",    lowerCamelCase		:       Tuple="</s>",    lowerCamelCase		:       int="</s>",    lowerCamelCase		:       Optional[int]="<s>",    lowerCamelCase		:       List[str]="<unk>",    lowerCamelCase		:       Any="<pad>",    lowerCamelCase		:       str="<mask>",    lowerCamelCase		:       Optional[int]=False,    lowerCamelCase		:       int=True,    **lowerCamelCase		:       Union[str, Any],    ):

								'''simple docstring'''
								super().__init__(
								    lowerCamelCase,    lowerCamelCase,    tokenizer_file=lowerCamelCase,    errors=lowerCamelCase,    bos_token=lowerCamelCase,    eos_token=lowerCamelCase,    sep_token=lowerCamelCase,    cls_token=lowerCamelCase,    unk_token=lowerCamelCase,    pad_token=lowerCamelCase,    mask_token=lowerCamelCase,    add_prefix_space=lowerCamelCase,    trim_offsets=lowerCamelCase,    **lowerCamelCase,    )

								lowercase__  					=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
								if pre_tok_state.get('''add_prefix_space''',    lowerCamelCase    ) != add_prefix_space:
												lowercase__  					=     getattr(lowerCamelCase,    pre_tok_state.pop('''type'''    )    )
												lowercase__  					=     add_prefix_space
												lowercase__  					=     pre_tok_class(**lowerCamelCase    )

								lowercase__  					=     add_prefix_space

								# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
								lowercase__  					=     '''post_processor'''
								lowercase__  					=     getattr(self.backend_tokenizer,    lowerCamelCase,    lowerCamelCase    )
								if tokenizer_component_instance:
												lowercase__  					=     json.loads(tokenizer_component_instance.__getstate__()    )

												# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
												if "sep" in state:
																lowercase__  					=     tuple(state['''sep''']    )
												if "cls" in state:
																lowercase__  					=     tuple(state['''cls''']    )

												lowercase__  					=     False

												if state.get('''add_prefix_space''',    lowerCamelCase    ) != add_prefix_space:
																lowercase__  					=     add_prefix_space
																lowercase__  					=     True

												if state.get('''trim_offsets''',    lowerCamelCase    ) != trim_offsets:
																lowercase__  					=     trim_offsets
																lowercase__  					=     True

												if changes_to_apply:
																lowercase__  					=     getattr(lowerCamelCase,    state.pop('''type'''    )    )
																lowercase__  					=     component_class(**lowerCamelCase    )
																setattr(self.backend_tokenizer,    lowerCamelCase,    lowerCamelCase    )




				@property
				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								if self._mask_token is None:
												if self.verbose:
																logger.error('''Using mask_token, but it is not set yet.'''    )
												return None
								return str(self._mask_token    )




				@mask_token.setter
				def 							lowercase__  (	self		:       List[str],    lowerCamelCase		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     AddedToken(lowerCamelCase,    lstrip=lowerCamelCase,    rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase,    lowerCamelCase    ) else value
								lowercase__  					=     value




				def 							lowercase__  (	self		:       Dict,    *lowerCamelCase		:       Dict,    **lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     kwargs.get('''is_split_into_words''',    lowerCamelCase    )

								if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    '''to use it with pretokenized inputs.'''    )

								return super()._batch_encode_plus(*lowerCamelCase,    **lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str],    *lowerCamelCase		:       Optional[int],    **lowerCamelCase		:       str    ):

								'''simple docstring'''
								lowercase__  					=     kwargs.get('''is_split_into_words''',    lowerCamelCase    )

								if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    '''to use it with pretokenized inputs.'''    )

								return super()._encode_plus(*lowerCamelCase,    **lowerCamelCase    )




				def 							lowercase__  (	self		:       int,    lowerCamelCase		:       str,    lowerCamelCase		:       Optional[str] = None    ):

								'''simple docstring'''
								lowercase__  					=     self._tokenizer.model.save(lowerCamelCase,    name=lowerCamelCase    )
								return tuple(lowerCamelCase    )




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       Any,    lowerCamelCase		:       List[str]=None    ):

								'''simple docstring'''
								lowercase__  					=     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
												return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       List[int],    lowerCamelCase		:       Optional[List[int]] = None    ):

								'''simple docstring'''
								lowercase__  					=     [self.sep_token_id]
								lowercase__  					=     [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 + sep + token_ids_a + sep    ) * [0]












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  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				@slow
				@require_torch
				def 							lowercase__  (	self		:       Optional[int]    ):

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

												return batch

								def _compute_metrics(lowerCamelCase		:       Any    ):
												lowercase__  					=     pred.label_ids
												lowercase__  					=     pred.predictions

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

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

												return {"accuracy": accuracy}

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

								lowercase__  					=     self.get_auto_remove_tmp_dir()

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

								# start training
								trainer.train()




