





'''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_rembert import RemBertTokenizer
else:
			__lowerCamelCase : List[Any]											=			None

__lowerCamelCase : Dict											=			logging.get_logger(__name__)
__lowerCamelCase : List[str]											=			{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

__lowerCamelCase : Optional[int]											=			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

__lowerCamelCase : Union[str, Any]											=			{
    "google/rembert": 256,
}

__lowerCamelCase : str											=			"▁"

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Tuple 			=		VOCAB_FILES_NAMES
		UpperCAmelCase					:	Optional[Any] 			=		PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase					:	Optional[int] 			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase					:	Optional[Any] 			=		RemBertTokenizer




		def __init__(self      :	Union[str, Any]							,			A__      :	Tuple=None							,			A__      :	Any=None							,			A__      :	List[Any]=True							,			A__      :	int=True							,			A__      :	List[str]=False							,			A__      :	List[str]="[CLS]"							,			A__      :	str="[SEP]"							,			A__      :	List[Any]="<unk>"							,			A__      :	Union[str, Any]="[SEP]"							,			A__      :	Tuple="<pad>"							,			A__      :	int="[CLS]"							,			A__      :	str="[MASK]"							,			**A__      :	Union[str, Any]							,			)					-> List[Any]:
						# Mask token behave like a normal word, i.e. include the space before it
						lowercase         = AddedToken(A__							,			lstrip=A__							,			rstrip=A__				) if isinstance(A__							,			A__				) else mask_token

						super().__init__(
						    A__							,			tokenizer_file=A__							,			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__							,			**A__							,			)

						lowercase         = do_lower_case
						lowercase         = remove_space
						lowercase         = keep_accents
						lowercase         = vocab_file
						lowercase         = False if not self.vocab_file else True




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	List[int]							,			A__      :	Optional[List[int]] = None				)					-> List[int]:
						lowercase         = [self.sep_token_id]
						lowercase         = [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      :	Optional[Any]							,			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 not None:
										return [1] + ([0] * len(A__				)) + [1] + ([0] * len(A__				)) + [1]
						return [1] + ([0] * len(A__				)) + [1]




		def 				UpperCAmelCase__   (self      :	Optional[int]							,			A__      :	List[int]							,			A__      :	Optional[List[int]] = None				)					-> List[int]:
						lowercase         = [self.sep_token_id]
						lowercase         = [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      :	List[Any]							,			A__      :	str							,			A__      :	Optional[str] = None				)					-> Tuple[str]:
						if not os.path.isdir(A__				):
										logger.error("Vocabulary path ({}) should be a directory".format(A__				)				)
										return
						lowercase         = 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_clip import CLIPImageProcessor


__lowerCamelCase : Union[str, Any]											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Tuple							,			*A__      :	Union[str, Any]							,			**A__      :	Optional[Any]				)					-> None:
						warnings.warn(
						    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use CLIPImageProcessor instead."							,			A__							,			)
						super().__init__(*A__							,			**A__				)




