






import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


lowercase  							=					logging.get_logger(__name__)




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




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













import os
import re
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": {
        "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"
        ),
    }
}

lowercase  							=					{
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= VOCAB_FILES_NAMES
	lowerCAmelCase													= PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase													= ['''input_ids''', '''attention_mask''']
	lowerCAmelCase													= []




	def __init__(       self       ,			a       ,			a="<unk>"       ,			a="<s>"       ,			a="</s>"       ,			a="<pad>"       ,			a="[SEP]"       ,			a="[MASK]"       ,			a="[CLS]"       ,			a = None       ,			**a       ,			)       ->    None:
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else bos_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else eos_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else unk_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else pad_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else cls_token
								snake_case_				=							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_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else mask_token

								snake_case_				=							{} if sp_model_kwargs is None else sp_model_kwargs

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

								snake_case_				=							vocab_file

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




	@property
	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								return self.sp_model.get_piece_size()




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								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					)       ->    Optional[int]:
								snake_case_				=							self.__dict__.copy()
								snake_case_				=							None
								return state




	def __setstate__(       self       ,			a					)       ->    Optional[Any]:
								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					)       ->    List[str]:
								return self.sp_model.encode(a       ,			out_type=a					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    Dict:
								return self.sp_model.piece_to_id(a					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    Union[str, Any]:
								snake_case_				=							self.sp_model.IdToPiece(a					)
								return token




	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 = False       ,			a = None       ,			a = True       ,			**a       ,			)       ->    str:
								snake_case_				=							kwargs.pop('use_source_tokenizer'       ,			a					)

								snake_case_				=							self.convert_ids_to_tokens(a       ,			skip_special_tokens=a					)

								# To avoid mixing byte-level and unicode for byte-level BPT
								# we need to build string separately for added tokens and byte-level tokens
								# cf. https://github.com/huggingface/transformers/issues/1133
								snake_case_				=							[]
								snake_case_				=							[]
								for token in filtered_tokens:
															if skip_special_tokens and token in self.all_special_ids:
																						continue
															if token in self.added_tokens_encoder:
																						if current_sub_text:
																													sub_texts.append(self.convert_tokens_to_string(a					)					)
																													snake_case_				=							[]
																						sub_texts.append(a					)
															else:
																						current_sub_text.append(a					)
								if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(a					)					)

								# Mimic the behavior of the Rust tokenizer:
								# No space before [MASK] and [SEP]
								if spaces_between_special_tokens:
															snake_case_				=							re.sub(R' (\[(MASK|SEP)\])'       ,			R'\1'       ,			' '.join(a					)					)
								else:
															snake_case_				=							''.join(a					)

								snake_case_				=							(
								    clean_up_tokenization_spaces
								    if clean_up_tokenization_spaces is not None
								    else self.clean_up_tokenization_spaces
								)
								if clean_up_tokenization_spaces:
															snake_case_				=							self.clean_up_tokenization(a					)
															return clean_text
								else:
															return text




	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,)




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[int]:
								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								snake_case_				=							[self.cls_token_id]
								snake_case_				=							[self.sep_token_id]
								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 None:
															return [1] + ([0] * len(a					)) + [1]
								return [1] + ([0] * len(a					)) + [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]





