
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:
							lowerCamelCase_  :	Optional[int] 				=					None

lowerCamelCase_  :	Optional[int] 				=					logging.get_logger(__name__)
lowerCamelCase_  :	Any 				=					{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_  :	List[str] 				=					{
    """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""",
    },
}

lowerCamelCase_  :	Any 				=					{
    """albert-base-v1""": 5_12,
    """albert-large-v1""": 5_12,
    """albert-xlarge-v1""": 5_12,
    """albert-xxlarge-v1""": 5_12,
    """albert-base-v2""": 5_12,
    """albert-large-v2""": 5_12,
    """albert-xlarge-v2""": 5_12,
    """albert-xxlarge-v2""": 5_12,
}

lowerCamelCase_  :	Optional[int] 				=					"""▁"""

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  str    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  Dict    =	PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  Optional[Any]    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase :  Dict    =	AlbertTokenizer

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

							super().__init__(
							    snake_case_  ,		tokenizer_file=snake_case_  ,		do_lower_case=snake_case_  ,		remove_space=snake_case_  ,		keep_accents=snake_case_  ,		bos_token=snake_case_  ,		eos_token=snake_case_  ,		unk_token=snake_case_  ,		sep_token=snake_case_  ,		pad_token=snake_case_  ,		cls_token=snake_case_  ,		mask_token=snake_case_  ,		**snake_case_  ,		)

							UpperCamelCase_:							int		=  do_lower_case
							UpperCamelCase_:							Any		=  remove_space
							UpperCamelCase_:							Optional[Any]		=  keep_accents
							UpperCamelCase_:							Dict		=  vocab_file
							UpperCamelCase_:							Any		=  False if not self.vocab_file else True

			def  lowerCAmelCase__		(    self	:    List[str]  ,		snake_case_	:    List[int]  ,		snake_case_	:    Optional[List[int]] = None ):
							UpperCamelCase_:							str		=  [self.sep_token_id]
							UpperCamelCase_:							Optional[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  lowerCAmelCase__		(    self	:    Optional[Any]  ,		snake_case_	:    List[int]  ,		snake_case_	:    Optional[List[int]] = None ):
							UpperCamelCase_:							Union[str, Any]		=  [self.sep_token_id]
							UpperCamelCase_:							Dict		=  [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  lowerCAmelCase__		(    self	:    Optional[Any]  ,		snake_case_	:    str  ,		snake_case_	:    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(snake_case_ ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
											return
							UpperCamelCase_:							Union[str, Any]		=  os.path.join(
							    snake_case_  ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

							if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ):
											copyfile(self.vocab_file  ,		snake_case_ )

							return (out_vocab_file,)








from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Union[str, Any]    =	["""image_processor""", """tokenizer"""]
			__UpperCamelCase :  List[Any]    =	"""AutoImageProcessor"""
			__UpperCamelCase :  Tuple    =	"""AutoTokenizer"""

			def __init__(    self	:    Any  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    Any ):
							super().__init__(snake_case_  ,		snake_case_ )
							UpperCamelCase_:							int		=  self.image_processor

			def __call__(    self	:    str  ,		snake_case_	:    Optional[int]=None  ,		snake_case_	:    int=None  ,		snake_case_	:    Dict=None  ,		**snake_case_	:    Optional[int] ):

							if text is None and images is None:
											raise ValueError("""You have to specify either text or images. Both cannot be none.""" )

							if text is not None:
											UpperCamelCase_:							List[Any]		=  self.tokenizer(snake_case_  ,		return_tensors=snake_case_  ,		**snake_case_ )

							if images is not None:
											UpperCamelCase_:							Any		=  self.image_processor(snake_case_  ,		return_tensors=snake_case_  ,		**snake_case_ )

							if text is not None and images is not None:
											UpperCamelCase_:							Tuple		=  image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**snake_case_ )  ,		tensor_type=snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[str]  ,		*snake_case_	:    int  ,		**snake_case_	:    Optional[Any] ):
							return self.tokenizer.batch_decode(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    int  ,		*snake_case_	:    Optional[Any]  ,		**snake_case_	:    str ):
							return self.tokenizer.decode(*snake_case_  ,		**snake_case_ )




			@property
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							return ["input_ids", "attention_mask", "pixel_values"]






