






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_camembert import CamembertTokenizer
else:
	lowercase  							=					None


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowercase  							=					{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

lowercase  							=					{
    "camembert-base": 512,
}

lowercase  							=					"▁"




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													= CamembertTokenizer




	def __init__(       self       ,			a=None       ,			a=None       ,			a="<s>"       ,			a="</s>"       ,			a="</s>"       ,			a="<s>"       ,			a="<unk>"       ,			a="<pad>"       ,			a="<mask>"       ,			a=["<s>NOTUSED", "</s>NOTUSED"]       ,			**a       ,			)       ->    Optional[int]:
								# 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

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

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




	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 + sep + token_ids_a + sep




	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 + sep + token_ids_a + sep					) * [0]






	def 							_UpperCamelCase			(       self       ,			a       ,			a = 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_				=							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,)













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					)





