"""simple docstring"""



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

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

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


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

__lowercase		:		Any														=							list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__lowercase		:		str														=							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class      _A	:




    """simple docstring"""




    UpperCamelCase_	:					Dict							=					field(
        default=_UpperCAmelCase							, metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(_UpperCAmelCase					)}					)
    UpperCamelCase_	:					Optional[Any]							=					field(
        default=_UpperCAmelCase							, metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''}					)
    UpperCamelCase_	:					Optional[Any]							=					field(
        default=1_2_8							, metadata={
            '''help''': (
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            )
        }							, )
    UpperCamelCase_	:					Tuple							=					field(
        default=1_2_8							, metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''}							, )
    UpperCamelCase_	:					Union[str, Any]							=					field(
        default=6_4							, metadata={
            '''help''': (
                '''The maximum number of tokens for the question. Questions longer than this will '''
                '''be truncated to this length.'''
            )
        }							, )
    UpperCamelCase_	:					List[str]							=					field(
        default=3_0							, metadata={
            '''help''': (
                '''The maximum length of an answer that can be generated. This is needed because the start '''
                '''and end predictions are not conditioned on one another.'''
            )
        }							, )
    UpperCamelCase_	:					Tuple							=					field(
        default=_UpperCAmelCase							, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}					)
    UpperCamelCase_	:					str							=					field(
        default=_UpperCAmelCase							, metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''}					)
    UpperCamelCase_	:					int							=					field(
        default=0.0							, metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}					)
    UpperCamelCase_	:					Union[str, Any]							=					field(
        default=2_0							, metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}					)
    UpperCamelCase_	:					Optional[int]							=					field(
        default=0							, metadata={
            '''help''': (
                '''language id of input for language-specific xlm models (see'''
                ''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
            )
        }							, )
    UpperCamelCase_	:					List[Any]							=					field(default=1							, metadata={'''help''': '''multiple threads for converting example to features'''}					)






class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					List[str]							=					'''train'''
    UpperCamelCase_	:					Dict							=					'''dev'''






class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					int							=					4_2
    UpperCamelCase_	:					Union[str, Any]							=					4_2
    UpperCamelCase_	:					Dict							=					4_2
    UpperCamelCase_	:					Any							=					4_2
    def __init__(		self							:     Optional[int]	,      A_							:     SquadDataTrainingArguments	,      A_							:     PreTrainedTokenizer	,      A_							:     Optional[int] = None	,      A_							:     Union[str, Split] = Split.train	,      A_							:     Optional[bool] = False	,      A_							:     Optional[str] = None	,      A_							:     Optional[str] = "pt"	,      )    ->       Tuple:
         __snake_case				=							args
         __snake_case				=							is_language_sensitive
         __snake_case				=							SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
         if isinstance(__A	,      __A    ):
              try:
                   __snake_case				=							Split[mode]
              except KeyError:
                   raise KeyError('''mode is not a valid split name'''    )
         __snake_case				=							mode
         # Load data features from cache or dataset file
         __snake_case				=							"v2" if args.version_2_with_negative else "v1"
         __snake_case				=							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}_{version_tag}"	,      )

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         __snake_case				=							cached_features_file + ".lock"
         with FileLock(__A    ):
              if os.path.exists(__A    ) and not args.overwrite_cache:
                   __snake_case				=							time.time()
                   __snake_case				=							torch.load(__A    )

                   # Legacy cache files have only features, while new cache files
                   # will have dataset and examples also.
                   __snake_case				=							self.old_features["features"]
                   __snake_case				=							self.old_features.get('''dataset'''	,      __A    )
                   __snake_case				=							self.old_features.get('''examples'''	,      __A    )
                   logger.info(
                       f"Loading features from cached file {cached_features_file} [took %.3f s]"	,      time.time() - start    )

                   if self.dataset is None or self.examples is None:
                        logger.warning(
                            f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
                            ''' future run'''    )
              else:
                   if mode == Split.dev:
                        __snake_case				=							self.processor.get_dev_examples(args.data_dir    )
                   else:
                        __snake_case				=							self.processor.get_train_examples(args.data_dir    )

                   __snake_case				=							squad_convert_examples_to_features(
                       examples=self.examples	,      tokenizer=__A	,      max_seq_length=args.max_seq_length	,      doc_stride=args.doc_stride	,      max_query_length=args.max_query_length	,      is_training=mode == Split.train	,      threads=args.threads	,      return_dataset=__A	,      )

                   __snake_case				=							time.time()
                   torch.save(
                       {'''features''': self.features, '''dataset''': self.dataset, '''examples''': self.examples}	,      __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[Any]    )    ->       Dict:
         return len(self.features    )







    def __getitem__(		self							:     Tuple	,      A_							:     str    )    ->       List[str]:
         # Convert to Tensors and build dataset
         __snake_case				=							self.features[i]

         __snake_case				=							torch.tensor(feature.input_ids	,      dtype=torch.long    )
         __snake_case				=							torch.tensor(feature.attention_mask	,      dtype=torch.long    )
         __snake_case				=							torch.tensor(feature.token_type_ids	,      dtype=torch.long    )
         __snake_case				=							torch.tensor(feature.cls_index	,      dtype=torch.long    )
         __snake_case				=							torch.tensor(feature.p_mask	,      dtype=torch.float    )
         __snake_case				=							torch.tensor(feature.is_impossible	,      dtype=torch.float    )

         __snake_case				=							{
             "input_ids": input_ids,
             "attention_mask": attention_mask,
             "token_type_ids": token_type_ids,
         }

         if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
              del inputs["token_type_ids"]

         if self.args.model_type in ["xlnet", "xlm"]:
              inputs.update({'''cls_index''': cls_index, '''p_mask''': p_mask}    )
              if self.args.version_2_with_negative:
                   inputs.update({'''is_impossible''': is_impossible}    )
              if self.is_language_sensitive:
                   inputs.update({'''langs''': (torch.ones(input_ids.shape	,      dtype=torch.intaa    ) * self.args.lang_id)}    )

         if self.mode == Split.train:
              __snake_case				=							torch.tensor(feature.start_position	,      dtype=torch.long    )
              __snake_case				=							torch.tensor(feature.end_position	,      dtype=torch.long    )
              inputs.update({'''start_positions''': start_positions, '''end_positions''': end_positions}    )

         return inputs
"""simple docstring"""



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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowercase		:		List[str]														=							logging.get_logger(__name__)

__lowercase		:		Union[str, Any]														=							{
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
}

__lowercase		:		Optional[int]														=							{
    "vocab_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
    },
    "merges_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
    },
}

__lowercase		:		Dict														=							{
    "vinai/phobert-base": 256,
    "vinai/phobert-large": 256,
}





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							set()
     __snake_case				=							word[0]
     for char in word[1:]:
          pairs.add((prev_char, char))
          __snake_case				=							char

     __snake_case				=							set(snake_case)
     return pairs




class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Union[str, Any]							=					VOCAB_FILES_NAMES
    UpperCamelCase_	:					Any							=					PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase_	:					Optional[Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    def __init__(		self							:     Union[str, Any]	,      A_							:     Optional[Any]	,      A_							:     Optional[int]	,      A_							:     str="<s>"	,      A_							:     List[str]="</s>"	,      A_							:     List[Any]="</s>"	,      A_							:     List[str]="<s>"	,      A_							:     Optional[int]="<unk>"	,      A_							:     List[str]="<pad>"	,      A_							:     Any="<mask>"	,      **A_							:     int	,      )    ->       Tuple:
         super().__init__(
             bos_token=A_	,      eos_token=A_	,      unk_token=A_	,      sep_token=A_	,      cls_token=A_	,      pad_token=A_	,      mask_token=A_	,      **A_	,      )

         __snake_case				=							vocab_file
         __snake_case				=							merges_file

         __snake_case				=							{}
         __snake_case				=							0
         __snake_case				=							1
         __snake_case				=							2
         __snake_case				=							3

         self.add_from_file(A_    )

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

         with open(A_	,      encoding='''utf-8'''    ) as merges_handle:
              __snake_case				=							merges_handle.read().split('''\n'''    )[:-1]
         __snake_case				=							[tuple(merge.split()[:-1]    ) for merge in merges]
         __snake_case				=							dict(zip(A_	,      range(len(A_    )    )    )    )
         __snake_case				=							{}
    def  lowercase					(		self							:     str	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:

         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __snake_case				=							[self.cls_token_id]
         __snake_case				=							[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
    def  lowercase					(		self							:     Dict	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None	,      A_							:     bool = False    )    ->       List[int]:

         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=A_	,      token_ids_a=A_	,      already_has_special_tokens=A_    )

         if token_ids_a is None:
              return [1] + ([0] * len(A_    )) + [1]
         return [1] + ([0] * len(A_    )) + [1, 1] + ([0] * len(A_    )) + [1]
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:
         __snake_case				=							[self.sep_token_id]
         __snake_case				=							[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]
    @property
    def  lowercase					(		self							:     List[Any]    )    ->       str:
         return len(self.encoder    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Optional[int]:
         return dict(self.encoder	,      **self.added_tokens_encoder    )
    def  lowercase					(		self							:     str	,      A_							:     List[Any]    )    ->       Optional[int]:
         if token in self.cache:
              return self.cache[token]
         __snake_case				=							tuple(A_    )
         __snake_case				=							tuple(list(word[:-1]    ) + [word[-1] + '''</w>''']    )
         __snake_case				=							get_pairs(A_    )

         if not pairs:
              return token

         while True:
              __snake_case				=							min(A_	,      key=lambda A_    : self.bpe_ranks.get(A_	,      float('''inf'''    )    )    )
              if bigram not in self.bpe_ranks:
                   break
              __snake_case		,	__snake_case				=							bigram
              __snake_case				=							[]
              __snake_case				=							0
              while i < len(A_    ):
                   try:
                        __snake_case				=							word.index(A_	,      A_    )
                   except ValueError:
                        new_word.extend(word[i:]    )
                        break
                   else:
                        new_word.extend(word[i:j]    )
                        __snake_case				=							j

                   if word[i] == first and i < len(A_    ) - 1 and word[i + 1] == second:
                        new_word.append(first + second    )
                        i += 2
                   else:
                        new_word.append(word[i]    )
                        i += 1
              __snake_case				=							tuple(A_    )
              __snake_case				=							new_word
              if len(A_    ) == 1:
                   break
              else:
                   __snake_case				=							get_pairs(A_    )
         __snake_case				=							'''@@ '''.join(A_    )
         __snake_case				=							word[:-4]
         __snake_case				=							word
         return word
    def  lowercase					(		self							:     int	,      A_							:     Tuple    )    ->       int:
         __snake_case				=							[]

         __snake_case				=							re.findall(R'''\S+\n?'''	,      A_    )

         for token in words:
              split_tokens.extend(list(self.bpe(A_    ).split(''' '''    )    )    )
         return split_tokens
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     Tuple    )    ->       int:
         return self.encoder.get(A_	,      self.encoder.get(self.unk_token    )    )
    def  lowercase					(		self							:     int	,      A_							:     Dict    )    ->       Optional[Any]:
         return self.decoder.get(A_	,      self.unk_token    )
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     int    )    ->       Tuple:
         __snake_case				=							''' '''.join(A_    ).replace('''@@ '''	,      ''''''    ).strip()
         return out_string
    def  lowercase					(		self							:     str	,      A_							:     str	,      A_							:     Optional[str] = None    )    ->       Tuple[str]:
         if not os.path.isdir(A_    ):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
              return
         __snake_case				=							os.path.join(
             A_	,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
         __snake_case				=							os.path.join(
             A_	,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

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

         if os.path.abspath(self.merges_file    ) != os.path.abspath(A_    ):
              copyfile(self.merges_file	,      A_    )

         return out_vocab_file, out_merge_file







    def  lowercase					(		self							:     Optional[int]	,      A_							:     Union[str, Any]    )    ->       Dict:
         if isinstance(A_	,      A_    ):
              try:
                   with open(A_	,      '''r'''	,      encoding='''utf-8'''    ) as fd:
                        self.add_from_file(A_    )
              except FileNotFoundError as fnfe:
                   raise fnfe
              except UnicodeError:
                   raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset"    )
              return

         __snake_case				=							f.readlines()
         for lineTmp in lines:
              __snake_case				=							lineTmp.strip()
              __snake_case				=							line.rfind(''' '''    )
              if idx == -1:
                   raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\''''    )
              __snake_case				=							line[:idx]
              __snake_case				=							len(self.encoder    )