






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase  							=					logging.get_logger(__name__)
lowercase  							=					{"vocab_file": "spiece.model"}

lowercase  							=					{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

lowercase  							=					{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

lowercase  							=					"▁"




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= VOCAB_FILES_NAMES
	lowerCAmelCase													= PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




	def __init__(       self       ,			a       ,			a=True       ,			a=True       ,			a=False       ,			a="[CLS]"       ,			a="[SEP]"       ,			a="<unk>"       ,			a="[SEP]"       ,			a="<pad>"       ,			a="[CLS]"       ,			a="[MASK]"       ,			a = None       ,			**a       ,			)       ->    None:
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								snake_case_				=							(
								    AddedToken(a       ,			lstrip=a       ,			rstrip=a       ,			normalized=a					)
								    if isinstance(a       ,			a					)
								    else mask_token
								)

								snake_case_				=							{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=a       ,			remove_space=a       ,			keep_accents=a       ,			bos_token=a       ,			eos_token=a       ,			unk_token=a       ,			sep_token=a       ,			pad_token=a       ,			cls_token=a       ,			mask_token=a       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**a       ,			)

								snake_case_				=							do_lower_case
								snake_case_				=							remove_space
								snake_case_				=							keep_accents
								snake_case_				=							vocab_file

								snake_case_				=							spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(a					)




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




	def 							_UpperCamelCase			(       self					)       ->    str:
								snake_case_				=							{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]:
								snake_case_				=							self.__dict__.copy()
								snake_case_				=							None
								return state




	def __setstate__(       self       ,			a					)       ->    int:
								snake_case_				=							d

								# for backward compatibility
								if not hasattr(self       ,			'sp_model_kwargs'					):
															snake_case_				=							{}

								snake_case_				=							spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(self.vocab_file					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    int:
								if self.remove_space:
															snake_case_				=							' '.join(inputs.strip().split()					)
								else:
															snake_case_				=							inputs
								snake_case_				=							outputs.replace('``'       ,			'"'					).replace('\'\''       ,			'"'					)

								if not self.keep_accents:
															snake_case_				=							unicodedata.normalize('NFKD'       ,			a					)
															snake_case_				=							''.join([c for c in outputs if not unicodedata.combining(a					)]					)
								if self.do_lower_case:
															snake_case_				=							outputs.lower()

								return outputs




	def 							_UpperCamelCase			(       self       ,			a					)       ->    List[str]:
								snake_case_				=							self.preprocess_text(a					)
								snake_case_				=							self.sp_model.encode(a       ,			out_type=a					)
								snake_case_				=							[]
								for piece in pieces:
															if len(a					) > 1 and piece[-1] == str(','					) and piece[-2].isdigit():
																						snake_case_				=							self.sp_model.EncodeAsPieces(piece[:-1].replace(a       ,			''					)					)
																						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																													if len(cur_pieces[0]					) == 1:
																																				snake_case_				=							cur_pieces[1:]
																													else:
																																				snake_case_				=							cur_pieces[0][1:]
																						cur_pieces.append(piece[-1]					)
																						new_pieces.extend(a					)
															else:
																						new_pieces.append(a					)

								return new_pieces




	def 							_UpperCamelCase			(       self       ,			a					)       ->    List[Any]:
								return self.sp_model.PieceToId(a					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    List[str]:
								return self.sp_model.IdToPiece(a					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    List[Any]:
								snake_case_				=							[]
								snake_case_				=							''
								snake_case_				=							False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						if not prev_is_special:
																													out_string += " "
																						out_string += self.sp_model.decode(a					) + token
																						snake_case_				=							True
																						snake_case_				=							[]
															else:
																						current_sub_tokens.append(a					)
																						snake_case_				=							False
								out_string += self.sp_model.decode(a					)
								return out_string.strip()




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[int]:
								snake_case_				=							[self.sep_token_id]
								snake_case_				=							[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       ,			a       ,			a = None       ,			a = 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 not None:
															return [1] + ([0] * len(a					)) + [1] + ([0] * len(a					)) + [1]
								return [1] + ([0] * len(a					)) + [1]




	def 							_UpperCamelCase			(       self       ,			a       ,			a = 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					) * [0] + len(token_ids_a + sep					) * [1]






	def 							_UpperCamelCase			(       self       ,			a       ,			a = 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']					)

								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_				=							self.sp_model.serialized_model_proto()
																						fi.write(a					)

								return (out_vocab_file,)













import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


lowercase  							=					logging.get_logger(__name__)




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	def __init__(       self       ,			*a       ,			**a					)       ->    None:
								warnings.warn(
								    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
								    ' Please use PerceiverImageProcessor instead.'       ,			a       ,			)
								super().__init__(*a       ,			**a					)





