"""simple docstring"""







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:
	_a					:					List[Any]=			None

_a					:					List[Any]=			logging.get_logger(__name__)
_a					:					Optional[Any]=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_a					:					Any=			{
    "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"
        ),
    },
}

_a					:					int=			{
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}


_a					:					List[str]=			"▁"

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				int   =		VOCAB_FILES_NAMES
				UpperCAmelCase    :				int   =		PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase    :				List[str]   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase    :				Dict   =		BigBirdTokenizer
				UpperCAmelCase    :				List[Any]   =		["""input_ids""", """attention_mask"""]
				UpperCAmelCase    :				List[int]   =		[]



				def __init__(self    :				Dict     ,  _A    :				List[str]=None     ,  _A    :				List[str]=None     ,  _A    :				Any="<unk>"     ,  _A    :				Optional[Any]="<s>"     ,  _A    :				Union[str, Any]="</s>"     ,  _A    :				int="<pad>"     ,  _A    :				List[str]="[SEP]"     ,  _A    :				Dict="[MASK]"     ,  _A    :				str="[CLS]"     ,  **_A    :				List[str]     ,  )   ->     int:
					__snake_case      :							List[str]						=			AddedToken(_A     ,  lstrip=_A     ,  rstrip=_A) if isinstance(_A     ,  _A) else bos_token
					__snake_case      :							Dict						=			AddedToken(_A     ,  lstrip=_A     ,  rstrip=_A) if isinstance(_A     ,  _A) else eos_token
					__snake_case      :							List[str]						=			AddedToken(_A     ,  lstrip=_A     ,  rstrip=_A) if isinstance(_A     ,  _A) else unk_token
					__snake_case      :							Optional[int]						=			AddedToken(_A     ,  lstrip=_A     ,  rstrip=_A) if isinstance(_A     ,  _A) else pad_token
					__snake_case      :							int						=			AddedToken(_A     ,  lstrip=_A     ,  rstrip=_A) if isinstance(_A     ,  _A) else cls_token
					__snake_case      :							Any						=			AddedToken(_A     ,  lstrip=_A     ,  rstrip=_A) if isinstance(_A     ,  _A) else sep_token

					# Mask token behave like a normal word, i.e. include the space before it
					__snake_case      :							Optional[int]						=			AddedToken(_A     ,  lstrip=_A     ,  rstrip=_A) if isinstance(_A     ,  _A) else mask_token

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

					__snake_case      :							List[Any]						=			vocab_file
					__snake_case      :							Any						=			False if not self.vocab_file else True



				def  _lowercase					(self    :				str     ,  _A    :				List[int]     ,  _A    :				Optional[List[int]] = None)   ->     List[int]:
					__snake_case      :							Optional[int]						=			[self.sep_token_id]
					__snake_case      :							int						=			[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  _lowercase					(self    :				Tuple     ,  _A    :				List[int]     ,  _A    :				Optional[List[int]] = None     ,  _A    :				bool = False)   ->     List[int]:

					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(_A)) + [1]
					return [1] + ([0] * len(_A)) + [1] + ([0] * len(_A)) + [1]



				def  _lowercase					(self    :				str     ,  _A    :				List[int]     ,  _A    :				Optional[List[int]] = None)   ->     List[int]:
					__snake_case      :							Dict						=			[self.sep_token_id]
					__snake_case      :							str						=			[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  _lowercase					(self    :				List[Any]     ,  _A    :				str     ,  _A    :				Optional[str] = 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
					__snake_case      :							Optional[Any]						=			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,)






"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


_a					:					Tuple=			logging.get_logger(__name__)

class 		UpperCamelCase      (   lowercase						):



				def __init__(self    :				int     ,  *_A    :				str     ,  **_A    :				List[str])   ->     None:
					warnings.warn(
					    'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
					    ' Please use VideoMAEImageProcessor instead.'     ,  _A     ,  )
					super().__init__(*_A     ,  **_A)





