

"""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


__UpperCAmelCase			=					logging.get_logger(__name__)

__UpperCAmelCase			=					list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__UpperCAmelCase			=					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class   __lowercase      :
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={"""help""": """Model type selected in the list: """ + """, """.join(__lowerCamelCase       )}       )
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""}       )
  snake_case_												=							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."""
          )
      }						,      )
  snake_case_												=							field(
      default=1_2_8						,      metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""}						,      )
  snake_case_												=							field(
      default=6_4						,      metadata={
          """help""": (
              """The maximum number of tokens for the question. Questions longer than this will """
              """be truncated to this length."""
          )
      }						,      )
  snake_case_												=							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."""
          )
      }						,      )
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={"""help""": """Overwrite the cached training and evaluation sets"""}       )
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""}       )
  snake_case_												=							field(
      default=0.0						,      metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""}       )
  snake_case_												=							field(
      default=2_0						,      metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""}       )
  snake_case_												=							field(
      default=0						,      metadata={
          """help""": (
              """language id of input for language-specific xlm models (see"""
              """ tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
          )
      }						,      )
  snake_case_												=							field(default=1						,      metadata={"""help""": """multiple threads for converting example to features"""}       )







class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							"""train"""
  snake_case_												=							"""dev"""







class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							42
  snake_case_												=							42
  snake_case_												=							42
  snake_case_												=							42

  def __init__(	self		:      List[str]       ,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"       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       args
   UpperCAmelCase__       :	Optional[Any]        =       is_language_sensitive
   UpperCAmelCase__       :	str        =       SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
   if isinstance(A       ,A     ):
    try:
     UpperCAmelCase__       :	int        =       Split[mode]
    except KeyError:
     raise KeyError("""mode is not a valid split name"""     )
   UpperCAmelCase__       :	List[Any]        =       mode
   # Load data features from cache or dataset file
   UpperCAmelCase__       :	str        =       """v2""" if args.version_2_with_negative else """v1"""
   UpperCAmelCase__       :	List[str]        =       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.
   UpperCAmelCase__       :	int        =       cached_features_file + """.lock"""
   with FileLock(A     ):
    if os.path.exists(A     ) and not args.overwrite_cache:
     UpperCAmelCase__       :	Optional[Any]        =       time.time()
     UpperCAmelCase__       :	List[Any]        =       torch.load(A     )

     # Legacy cache files have only features, while new cache files
     # will have dataset and examples also.
     UpperCAmelCase__       :	Any        =       self.old_features["""features"""]
     UpperCAmelCase__       :	Tuple        =       self.old_features.get("""dataset"""       ,A     )
     UpperCAmelCase__       :	Tuple        =       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:
      UpperCAmelCase__       :	Tuple        =       self.processor.get_dev_examples(args.data_dir     )
     else:
      UpperCAmelCase__       :	Dict        =       self.processor.get_train_examples(args.data_dir     )

     UpperCAmelCase__ ,   UpperCAmelCase__       :	List[str]        =       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       ,)

     UpperCAmelCase__       :	str        =       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		:      List[Any]     ):






   '''simple docstring'''
   return len(self.features     )




  def __getitem__(	self		:      str       ,A		:      List[Any]     ):






   '''simple docstring'''
   # Convert to Tensors and build dataset
   UpperCAmelCase__       :	Optional[Any]        =       self.features[i]

   UpperCAmelCase__       :	Any        =       torch.tensor(feature.input_ids       ,dtype=torch.long     )
   UpperCAmelCase__       :	int        =       torch.tensor(feature.attention_mask       ,dtype=torch.long     )
   UpperCAmelCase__       :	Tuple        =       torch.tensor(feature.token_type_ids       ,dtype=torch.long     )
   UpperCAmelCase__       :	List[Any]        =       torch.tensor(feature.cls_index       ,dtype=torch.long     )
   UpperCAmelCase__       :	Optional[int]        =       torch.tensor(feature.p_mask       ,dtype=torch.float     )
   UpperCAmelCase__       :	Optional[int]        =       torch.tensor(feature.is_impossible       ,dtype=torch.float     )

   UpperCAmelCase__       :	List[str]        =       {
       """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:
    UpperCAmelCase__       :	Union[str, Any]        =       torch.tensor(feature.start_position       ,dtype=torch.long     )
    UpperCAmelCase__       :	Dict        =       torch.tensor(feature.end_position       ,dtype=torch.long     )
    inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions}     )

   return inputs








"""simple docstring"""
import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A							=  """▁"""

_A							=  get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_sentencepiece
@require_tokenizers
class       lowerCamelCase	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  BigBirdTokenizer
  SCREAMING_SNAKE_CASE					      =  BigBirdTokenizerFast
  SCREAMING_SNAKE_CASE					      =  True
  SCREAMING_SNAKE_CASE					      =  True



  def  _a      (self	):


        """simple docstring"""

        super().setUp()

        UpperCAmelCase__			:							Any							=    self.tokenizer_class(_lowerCamelCase							,       keep_accents=_lowerCamelCase	)
        tokenizer.save_pretrained(self.tmpdirname	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    """<s>"""
        UpperCAmelCase__			:							Optional[int]							=    1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase	)							,       _lowerCamelCase	)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase	)							,       _lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    list(self.get_tokenizer().get_vocab().keys()	)

        self.assertEqual(vocab_keys[0]							,       """<unk>"""	)
        self.assertEqual(vocab_keys[1]							,       """<s>"""	)
        self.assertEqual(vocab_keys[-1]							,       """[MASK]"""	)
        self.assertEqual(len(_lowerCamelCase	)							,       1004	)



  def  _a      (self	):


        """simple docstring"""

        self.assertEqual(self.get_tokenizer().vocab_size							,       1000	)



  def  _a      (self	):


        """simple docstring"""

        if not self.test_rust_tokenizer:
              return

        UpperCAmelCase__			:							List[Any]							=    self.get_tokenizer()
        UpperCAmelCase__			:							Optional[Any]							=    self.get_rust_tokenizer()

        UpperCAmelCase__			:							Dict							=    """I was born in 92000, and this is falsé."""

        UpperCAmelCase__			:							Dict							=    tokenizer.tokenize(_lowerCamelCase	)
        UpperCAmelCase__			:							int							=    rust_tokenizer.tokenize(_lowerCamelCase	)
        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)

        UpperCAmelCase__			:							int							=    tokenizer.encode(_lowerCamelCase							,       add_special_tokens=_lowerCamelCase	)
        UpperCAmelCase__			:							str							=    rust_tokenizer.encode(_lowerCamelCase							,       add_special_tokens=_lowerCamelCase	)
        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)

        UpperCAmelCase__			:							Any							=    self.get_rust_tokenizer()
        UpperCAmelCase__			:							List[Any]							=    tokenizer.encode(_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    rust_tokenizer.encode(_lowerCamelCase	)
        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    BigBirdTokenizer(_lowerCamelCase							,       keep_accents=_lowerCamelCase	)

        UpperCAmelCase__			:							Union[str, Any]							=    tokenizer.tokenize("""This is a test"""	)
        self.assertListEqual(_lowerCamelCase							,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(_lowerCamelCase	)							,       [285, 46, 10, 170, 382]							,       )

        UpperCAmelCase__			:							Dict							=    tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
        self.assertListEqual(
            _lowerCamelCase							,       [
                SPIECE_UNDERLINE + """I""",
                SPIECE_UNDERLINE + """was""",
                SPIECE_UNDERLINE + """b""",
                """or""",
                """n""",
                SPIECE_UNDERLINE + """in""",
                SPIECE_UNDERLINE + """""",
                """9""",
                """2""",
                """0""",
                """0""",
                """0""",
                """,""",
                SPIECE_UNDERLINE + """and""",
                SPIECE_UNDERLINE + """this""",
                SPIECE_UNDERLINE + """is""",
                SPIECE_UNDERLINE + """f""",
                """al""",
                """s""",
                """é""",
                """.""",
            ]							,       )
        UpperCAmelCase__			:							Tuple							=    tokenizer.convert_tokens_to_ids(_lowerCamelCase	)
        self.assertListEqual(
            _lowerCamelCase							,       [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]							,       )

        UpperCAmelCase__			:							Tuple							=    tokenizer.convert_ids_to_tokens(_lowerCamelCase	)
        self.assertListEqual(
            _lowerCamelCase							,       [
                SPIECE_UNDERLINE + """I""",
                SPIECE_UNDERLINE + """was""",
                SPIECE_UNDERLINE + """b""",
                """or""",
                """n""",
                SPIECE_UNDERLINE + """in""",
                SPIECE_UNDERLINE + """""",
                """<unk>""",
                """2""",
                """0""",
                """0""",
                """0""",
                """,""",
                SPIECE_UNDERLINE + """and""",
                SPIECE_UNDERLINE + """this""",
                SPIECE_UNDERLINE + """is""",
                SPIECE_UNDERLINE + """f""",
                """al""",
                """s""",
                """<unk>""",
                """.""",
            ]							,       )



  @cached_property
  def  _a      (self	):


        """simple docstring"""

        return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""	)



  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    """Hello World!"""
        UpperCAmelCase__			:							Union[str, Any]							=    [65, 18536, 2260, 101, 66]

        self.assertListEqual(_lowerCamelCase							,       self.big_tokenizer.encode(_lowerCamelCase	)	)



  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    (
            """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
            """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
        )
        # fmt: off
        UpperCAmelCase__			:							Dict							=    [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
        # fmt: on
        self.assertListEqual(_lowerCamelCase							,       self.big_tokenizer.encode(_lowerCamelCase	)	)



  @require_torch
  @slow
  def  _a      (self	):


        """simple docstring"""

        import torch

        from transformers import BigBirdConfig, BigBirdModel

        # Build sequence
        UpperCAmelCase__			:							int							=    list(self.big_tokenizer.get_vocab().keys()	)[:10]
        UpperCAmelCase__			:							str							=    """ """.join(_lowerCamelCase	)
        UpperCAmelCase__			:							int							=    self.big_tokenizer.encode_plus(_lowerCamelCase							,       return_tensors="""pt"""							,       return_token_type_ids=_lowerCamelCase	)
        UpperCAmelCase__			:							str							=    self.big_tokenizer.batch_encode_plus(
            [sequence + """ """ + sequence]							,       return_tensors="""pt"""							,       return_token_type_ids=_lowerCamelCase	)

        UpperCAmelCase__			:							Optional[int]							=    BigBirdConfig(attention_type="""original_full"""	)
        UpperCAmelCase__			:							Optional[int]							=    BigBirdModel(_lowerCamelCase	)

        assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

        with torch.no_grad():
              model(**_lowerCamelCase	)
              model(**_lowerCamelCase	)



  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""	)
        UpperCAmelCase__			:							int							=    tokenizer.decode(tokenizer("""Paris is the [MASK]."""	).input_ids	)

        self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]"""	)




  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    {"""input_ids""": [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=_lowerCamelCase							,       model_name="""google/bigbird-roberta-base"""							,       revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""							,       )

