"""simple docstring"""







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

import sentencepiece as spm

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


_a					:					str=			"▁"

_a					:					int=			{"vocab_file": "spiece.model"}

_a					:					Tuple=			{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_a					:					List[str]=			{
    "google/pegasus-xsum": 512,
}


_a					:					int=			logging.get_logger(__name__)

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				str   =		VOCAB_FILES_NAMES

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



				def __init__(self    :				Tuple     ,  _A    :				int     ,  _A    :				List[str]="<pad>"     ,  _A    :				List[str]="</s>"     ,  _A    :				Optional[Any]="<unk>"     ,  _A    :				Any="<mask_2>"     ,  _A    :				Optional[Any]="<mask_1>"     ,  _A    :				Union[str, Any]=None     ,  _A    :				Optional[int]=1_03     ,  _A    :				Optional[Any] = None     ,  **_A    :				List[str]     ,  )   ->     List[str]:
					__snake_case      :							str						=			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)}")

						__snake_case      :							str						=			(
						    ([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}.")
						__snake_case      :							Tuple						=			additional_special_tokens_extended
					else:
						__snake_case      :							Tuple						=			[mask_token_sent] if mask_token_sent is not None else []
						additional_special_tokens += [f"<unk_{i}>" for i in range(2     ,  self.offset)]

					__snake_case      :							str						=			{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    eos_token=_a     ,  unk_token=_a     ,  mask_token=_a     ,  pad_token=_a     ,  mask_token_sent=_a     ,  offset=_a     ,  additional_special_tokens=_a     ,  sp_model_kwargs=self.sp_model_kwargs     ,  **_a     ,  )
					__snake_case      :							Any						=			mask_token_sent
					__snake_case      :							List[str]						=			vocab_file
					__snake_case      :							List[Any]						=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(_a)

					# add special tokens to encoder dict
					__snake_case      :							Dict[int, str]						=			{
					    0: self.pad_token,
					    1: self.eos_token,
					}

					if self.mask_token_sent is not None:
						self.encoder.update(
						    {
						        2: self.mask_token_sent,
						        3: self.mask_token,
						    })

					if self.offset > 0:
						# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
						# mask_token_sent is already added to list -> so start at 1
						self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1     ,  self.offset - 1)})

					__snake_case      :							Dict[str, int]						=			{v: k for k, v in self.encoder.items()}



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Any:
					return len(self.sp_model) + self.offset



				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					__snake_case      :							Optional[int]						=			{self.convert_ids_to_tokens(_a): i for i in range(self.vocab_size)}
					vocab.update(self.added_tokens_encoder)
					return vocab



				def __getstate__(self    :				List[Any])   ->     Union[str, Any]:
					__snake_case      :							Dict						=			self.__dict__.copy()
					__snake_case      :							int						=			None
					return state



				def __setstate__(self    :				Union[str, Any]     ,  _A    :				List[Any])   ->     List[str]:
					__snake_case      :							List[Any]						=			d

					# for backward compatibility
					if not hasattr(self     ,  'sp_model_kwargs'):
						__snake_case      :							Optional[int]						=			{}

					__snake_case      :							List[str]						=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(self.vocab_file)



				def  _lowercase					(self    :				Optional[int]     ,  _A    :				int)   ->     Dict:
					return self.sp_model.encode(_a     ,  out_type=_a)



				def  _lowercase					(self    :				Any     ,  _A    :				str)   ->     Any:
					if token in self.decoder:
						return self.decoder[token]
					elif token in self.added_tokens_decoder:
						return self.added_tokens_decoder[token]
					__snake_case      :							Any						=			self.sp_model.piece_to_id(_a)
					return sp_id + self.offset



				def  _lowercase					(self    :				Dict     ,  _A    :				Optional[int])   ->     List[str]:
					if index in self.encoder:
						return self.encoder[index]
					elif index in self.added_tokens_encoder:
						return self.added_tokens_encoder[index]
					else:
						__snake_case      :							str						=			self.sp_model.IdToPiece(index - self.offset)
					return token



				def  _lowercase					(self    :				Any     ,  _A    :				int)   ->     Dict:
					__snake_case      :							Union[str, Any]						=			[]
					__snake_case      :							int						=			""
					for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
							out_string += self.sp_model.decode(_a) + token
							__snake_case      :							Optional[int]						=			[]
						else:
							current_sub_tokens.append(_a)
					out_string += self.sp_model.decode(_a)
					return out_string.strip()



				def  _lowercase					(self    :				str     ,  _A    :				Any=False)   ->     List[Any]:
					return 1



				def  _lowercase					(self    :				List[Any]     ,  _A    :				Optional[Any])   ->     int:
					__snake_case      :							Union[str, Any]						=			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

					return [1 if x in all_special_ids else 0 for x in seq]



				def  _lowercase					(self    :				List[str]     ,  _A    :				Any     ,  _A    :				Union[str, Any] = None     ,  _A    :				Optional[int] = False)   ->     Union[str, Any]:
					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    :				List[str]     ,  _A    :				str     ,  _A    :				str=None)   ->     List[str]:
					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    :				Union[str, Any]     ,  _A    :				int     ,  _A    :				Optional[int] = None)   ->     Union[str, Any]:
					if not os.path.isdir(_a):
						logger.error(f"Vocabulary path ({save_directory}) should be a directory")
						return
					__snake_case      :							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) and os.path.isfile(self.vocab_file):
						copyfile(self.vocab_file     ,  _a)
					elif not os.path.isfile(self.vocab_file):
						with open(_a     ,  'wb') as fi:
							__snake_case      :							Union[str, Any]						=			self.sp_model.serialized_model_proto()
							fi.write(_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)





