

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__				=		None


a__				=		logging.get_logger(__name__)


a__				=		'''▁'''

a__				=		{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a__				=		{
    '''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__				=		{
    '''google/pegasus-xsum''': 512,
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[int]	    =    VOCAB_FILES_NAMES
							UpperCAmelCase__       :						str	    =    PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__       :						Any	    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__       :						List[str]	    =    PegasusTokenizer
							UpperCAmelCase__       :						Union[str, Any]	    =    ["input_ids", "attention_mask"]


							def __init__(			self       ,							_a=None       ,							_a=None       ,							_a="<pad>"       ,							_a="</s>"       ,							_a="<unk>"       ,							_a="<mask_2>"       ,							_a="<mask_1>"       ,							_a=None       ,							_a=1_0_3       ,							**_a       ,							)    ->    Any:
												_a	:    Any         =		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       )}"""       )

																	_a	:    Tuple         =		(
																	    ([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}."""       )
																	_a	:    List[Any]         =		additional_special_tokens_extended
												else:
																	_a	:    List[str]         =		[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__(
												    _a       ,							tokenizer_file=_a       ,							pad_token=_a       ,							eos_token=_a       ,							unk_token=_a       ,							mask_token=_a       ,							mask_token_sent=_a       ,							offset=_a       ,							additional_special_tokens=_a       ,							**_a       ,							)
												_a	:    Optional[Any]         =		vocab_file
												_a	:    Any         =		False if not self.vocab_file else True


							def 				__lowercase				(			self       ,							_a       )    ->    Dict:
												_a	:    List[str]         =		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 				__lowercase				(			self       ,							_a       ,							_a = None       ,							_a = False       )    ->    List[int]:
												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 				__lowercase				(			self       ,							_a       ,							_a=None       )    ->    List[int]:
												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 				__lowercase				(			self       ,							_a       ,							_a = None       )    ->    Tuple[str]:
												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(_a       ):
																	logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
																	return
												_a	:    Tuple         =		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       ):
																	copyfile(self.vocab_file       ,							_a       )

												return (out_vocab_file,)






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


a__				=		logging.get_logger(__name__)

@dataclass
class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()   )}   )
							UpperCAmelCase__       :						str	    =    field(
							    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
							UpperCAmelCase__       :						int	    =    field(
							    default=128				,       metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }				,       )
							UpperCAmelCase__       :						bool	    =    field(
							    default=__lowercase				,       metadata={"help": "Overwrite the cached training and evaluation sets"}   )


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    List[Any]         =		self.task_name.lower()



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[str]	    =    "train"
							UpperCAmelCase__       :						List[str]	    =    "dev"
							UpperCAmelCase__       :						Union[str, Any]	    =    "test"



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						GlueDataTrainingArguments
							UpperCAmelCase__       :						str
							UpperCAmelCase__       :						List[InputFeatures]


							def __init__(			self       ,							_a       ,							_a       ,							_a = None       ,							_a = Split.train       ,							_a = None       ,							)    ->    str:
												warnings.warn(
												    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
												    '''library. You can have a look at this example script for pointers: '''
												    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''       ,							_a       ,							)
												_a	:    List[str]         =		args
												_a	:    List[str]         =		glue_processors[args.task_name]()
												_a	:    List[Any]         =		glue_output_modes[args.task_name]
												if isinstance(_a       ,							_a       ):
																	try:
																						_a	:    Any         =		Split[mode]
																	except KeyError:
																						raise KeyError('''mode is not a valid split name'''       )
        # Load data features from cache or dataset file
												_a	:    Union[str, Any]         =		os.path.join(
												    cache_dir if cache_dir is not None else args.data_dir       ,							F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""       ,							)
												_a	:    str         =		self.processor.get_labels()
												if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
												    "RobertaTokenizer",
												    "RobertaTokenizerFast",
												    "XLMRobertaTokenizer",
												    "BartTokenizer",
												    "BartTokenizerFast",
												):
																	# HACK(label indices are swapped in RoBERTa pretrained model)
																	_a      ,					_a	:    Optional[int]         =		label_list[2], label_list[1]
												_a	:    Dict         =		label_list

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												_a	:    Optional[int]         =		cached_features_file + '''.lock'''
												with FileLock(_a       ):
																	if os.path.exists(_a       ) and not args.overwrite_cache:
																						_a	:    List[Any]         =		time.time()
																						_a	:    str         =		torch.load(_a       )
																						logger.info(
																						    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""       ,							time.time() - start       )
																	else:
																						logger.info(F"""Creating features from dataset file at {args.data_dir}"""       )

																						if mode == Split.dev:
																											_a	:    str         =		self.processor.get_dev_examples(args.data_dir       )
																						elif mode == Split.test:
																											_a	:    Any         =		self.processor.get_test_examples(args.data_dir       )
																						else:
																											_a	:    List[str]         =		self.processor.get_train_examples(args.data_dir       )
																						if limit_length is not None:
																											_a	:    Dict         =		examples[:limit_length]
																						_a	:    Union[str, Any]         =		glue_convert_examples_to_features(
																						    _a       ,							_a       ,							max_length=args.max_seq_length       ,							label_list=_a       ,							output_mode=self.output_mode       ,							)
																						_a	:    Optional[int]         =		time.time()
																						torch.save(self.features       ,							_a       )
																						# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																						logger.info(
																						    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""       )


							def __len__(			self       )    ->    Optional[int]:
												return len(self.features       )


							def __getitem__(			self       ,							_a       )    ->    InputFeatures:
												return self.features[i]




							def 				__lowercase				(			self       )    ->    Tuple:
												return self.label_list



