


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_albert import AlbertTokenizer
else:
							lowercase_	   =     None

lowercase_	   =     logging.get_logger(__name__)
lowercase_	   =     {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

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""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

lowercase_	   =     {
    """albert-base-v1""": 5_1_2,
    """albert-large-v1""": 5_1_2,
    """albert-xlarge-v1""": 5_1_2,
    """albert-xxlarge-v1""": 5_1_2,
    """albert-base-v2""": 5_1_2,
    """albert-large-v2""": 5_1_2,
    """albert-xlarge-v2""": 5_1_2,
    """albert-xxlarge-v2""": 5_1_2,
}

lowercase_	   =     """▁"""


class 	A_ (	__UpperCamelCase   ):




						'''simple docstring'''


						__snake_case 	=       VOCAB_FILES_NAMES
						__snake_case 	=       PRETRAINED_VOCAB_FILES_MAP
						__snake_case 	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__snake_case 	=       AlbertTokenizer




						def __init__(    self:	Optional[Any]						,				a:	Optional[Any]=None						,				a:	List[Any]=None						,				a:	Tuple=True						,				a:	List[Any]=True						,				a:	Union[str, Any]=False						,				a:	Dict="[CLS]"						,				a:	List[Any]="[SEP]"						,				a:	str="<unk>"						,				a:	Any="[SEP]"						,				a:	List[str]="<pad>"						,				a:	Union[str, Any]="[CLS]"						,				a:	int="[MASK]"						,				**a:	Tuple						,				):
							# 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.
							__lowerCamelCase		:							Optional[int]		 =						(
							    AddedToken(a						,				lstrip=a						,				rstrip=a						,				normalized=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						,				)

							__lowerCamelCase		:							Dict		 =						do_lower_case
							__lowerCamelCase		:							Any		 =						remove_space
							__lowerCamelCase		:							str		 =						keep_accents
							__lowerCamelCase		:							Any		 =						vocab_file
							__lowerCamelCase		:							str		 =						False if not self.vocab_file else True




						def 					_snake_case     (    self:	List[str]						,				a:	List[int]						,				a:	Optional[List[int]] = None    ):
							__lowerCamelCase		:							Tuple		 =						[self.sep_token_id]
							__lowerCamelCase		:							List[Any]		 =						[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 					_snake_case     (    self:	Tuple						,				a:	List[int]						,				a:	Optional[List[int]] = None    ):
							__lowerCamelCase		:							Optional[int]		 =						[self.sep_token_id]
							__lowerCamelCase		:							Optional[Any]		 =						[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 					_snake_case     (    self:	Optional[int]						,				a:	str						,				a:	Optional[str] = None    ):
							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
							__lowerCamelCase		:							Tuple		 =						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_chinese_clip import ChineseCLIPImageProcessor


lowercase_	   =     logging.get_logger(__name__)


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      def __init__(    self:	List[str]						,				*a:	List[Any]						,				**a:	Optional[Any]    ):
       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    )

