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

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


if is_sentencepiece_available():
    from .tokenization_big_bird import BigBirdTokenizer
else:
    lowerCamelCase_						     =					None

lowerCamelCase_						     =					logging.get_logger(__name__)
lowerCamelCase_						     =					{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase_						     =					{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

lowerCamelCase_						     =					{
    "google/bigbird-roberta-base": 40_96,
    "google/bigbird-roberta-large": 40_96,
    "google/bigbird-base-trivia-itc": 40_96,
}


lowerCamelCase_						     =					"▁"




class      __A(    __lowerCAmelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__         =      PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__         =      BigBirdTokenizer
  SCREAMING_SNAKE_CASE__         =      ["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE__         =      []







  def __init__(self	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_="<unk>"	,      SCREAMING_SNAKE_CASE_="<s>"	,      SCREAMING_SNAKE_CASE_="</s>"	,      SCREAMING_SNAKE_CASE_="<pad>"	,      SCREAMING_SNAKE_CASE_="[SEP]"	,      SCREAMING_SNAKE_CASE_="[MASK]"	,      SCREAMING_SNAKE_CASE_="[CLS]"	,      **SCREAMING_SNAKE_CASE_	,      ):
       UpperCamelCase__				=    AddedToken(lowerCamelCase__	,      lstrip=lowerCamelCase__	,      rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__	,      lowerCamelCase__						) else bos_token
       UpperCamelCase__				=    AddedToken(lowerCamelCase__	,      lstrip=lowerCamelCase__	,      rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__	,      lowerCamelCase__						) else eos_token
       UpperCamelCase__				=    AddedToken(lowerCamelCase__	,      lstrip=lowerCamelCase__	,      rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__	,      lowerCamelCase__						) else unk_token
       UpperCamelCase__				=    AddedToken(lowerCamelCase__	,      lstrip=lowerCamelCase__	,      rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__	,      lowerCamelCase__						) else pad_token
       UpperCamelCase__				=    AddedToken(lowerCamelCase__	,      lstrip=lowerCamelCase__	,      rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__	,      lowerCamelCase__						) else cls_token
       UpperCamelCase__				=    AddedToken(lowerCamelCase__	,      lstrip=lowerCamelCase__	,      rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__	,      lowerCamelCase__						) else sep_token

       # Mask token behave like a normal word, i.e. include the space before it
       UpperCamelCase__				=    AddedToken(lowerCamelCase__	,      lstrip=lowerCamelCase__	,      rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__	,      lowerCamelCase__						) else mask_token

       super().__init__(
           lowerCamelCase__	,      tokenizer_file=lowerCamelCase__	,      bos_token=lowerCamelCase__	,      eos_token=lowerCamelCase__	,      unk_token=lowerCamelCase__	,      sep_token=lowerCamelCase__	,      pad_token=lowerCamelCase__	,      cls_token=lowerCamelCase__	,      mask_token=lowerCamelCase__	,      **lowerCamelCase__	,      )

       UpperCamelCase__				=    vocab_file
       UpperCamelCase__				=    False if not self.vocab_file else True







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None						):
       UpperCamelCase__				=    [self.sep_token_id]
       UpperCamelCase__				=    [self.cls_token_id]
       if token_ids_a is None:
            return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = False						):






       if already_has_special_tokens:
            if token_ids_a is not None:
                 raise ValueError(
                     """You should not supply a second sequence if the provided sequence of """
                     """ids is already formatted with special tokens for the model."""						)
            return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is None:
            return [1] + ([0] * len(lowerCamelCase__						)) + [1]
       return [1] + ([0] * len(lowerCamelCase__						)) + [1] + ([0] * len(lowerCamelCase__						)) + [1]







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None						):
       UpperCamelCase__				=    [self.sep_token_id]
       UpperCamelCase__				=    [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						) * [0] + len(token_ids_a + sep						) * [1]







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = 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(lowerCamelCase__						):
            logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
            return
       UpperCamelCase__				=    os.path.join(
           lowerCamelCase__	,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

       return (out_vocab_file,)







import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows






def 						__magic_name__			(   __a      :	Any						):  # picklable for multiprocessing



     '''simple docstring'''




     return i + 1






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 						__magic_name__			(   ):



     '''simple docstring'''




     with parallel_backend("""spark"""						):
          assert ParallelBackendConfig.backend_name == "spark"

     UpperCamelCase__				=    [1, 2, 3]
     with pytest.raises(__a						):
          with parallel_backend("""unsupported backend"""						):
               map_nested(__a	,		__a	,		num_proc=2						)

     with pytest.raises(__a						):
          with parallel_backend("""unsupported backend"""						):
               map_nested(__a	,		__a	,		num_proc=-1						)






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc"""	,		[2, -1]						)
def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    [1, 2]
     UpperCamelCase__				=    {"""a""": 1, """b""": 2}
     UpperCamelCase__				=    {"""a""": [1, 2], """b""": [3, 4]}
     UpperCamelCase__				=    {"""a""": {"""1""": 1}, """b""": 2}
     UpperCamelCase__				=    {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
     UpperCamelCase__				=    [2, 3]
     UpperCamelCase__				=    {"""a""": 2, """b""": 3}
     UpperCamelCase__				=    {"""a""": [2, 3], """b""": [4, 5]}
     UpperCamelCase__				=    {"""a""": {"""1""": 2}, """b""": 3}
     UpperCamelCase__				=    {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}

     with parallel_backend("""spark"""						):
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa





