


'''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_albert import AlbertTokenizer
else:
       lowerCAmelCase    :     int					=						None

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

lowerCAmelCase    :     Tuple					=						{
    """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    :     Dict					=						{
    """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__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    VOCAB_FILES_NAMES
     __magic_name__    			=    PRETRAINED_VOCAB_FILES_MAP
     __magic_name__    			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__    			=    AlbertTokenizer







     def __init__( self      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=False      ,	snake_case__="[CLS]"      ,	snake_case__="[SEP]"      ,	snake_case__="<unk>"      ,	snake_case__="[SEP]"      ,	snake_case__="<pad>"      ,	snake_case__="[CLS]"      ,	snake_case__="[MASK]"      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       (
                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__      ,	)

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







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       [self.sep_token_id]
            _lowerCAmelCase    :							Optional[int]   			=       [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 a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       [self.sep_token_id]
            _lowerCAmelCase    :							List[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 a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            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
            _lowerCAmelCase    :							Optional[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,)





'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase    :     str					=						logging.get_logger(__name__)

lowerCAmelCase    :     int					=						{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "data2vec-text"







     def __init__( self      ,	snake_case__=3_0522      ,	snake_case__=768      ,	snake_case__=12      ,	snake_case__=12      ,	snake_case__=3072      ,	snake_case__="gelu"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=512      ,	snake_case__=2      ,	snake_case__=0.02      ,	snake_case__=1E-12      ,	snake_case__=1      ,	snake_case__=0      ,	snake_case__=2      ,	snake_case__="absolute"      ,	snake_case__=True      ,	snake_case__=None      ,	**snake_case__      ,	):

            '''simple docstring'''

            super().__init__(pad_token_id=snake_case__      ,	bos_token_id=snake_case__      ,	eos_token_id=snake_case__      ,	**snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       vocab_size
            _lowerCAmelCase    :							Tuple   			=       hidden_size
            _lowerCAmelCase    :							Dict   			=       num_hidden_layers
            _lowerCAmelCase    :							int   			=       num_attention_heads
            _lowerCAmelCase    :							str   			=       hidden_act
            _lowerCAmelCase    :							Any   			=       intermediate_size
            _lowerCAmelCase    :							Any   			=       hidden_dropout_prob
            _lowerCAmelCase    :							Optional[int]   			=       attention_probs_dropout_prob
            _lowerCAmelCase    :							str   			=       max_position_embeddings
            _lowerCAmelCase    :							Any   			=       type_vocab_size
            _lowerCAmelCase    :							int   			=       initializer_range
            _lowerCAmelCase    :							List[str]   			=       layer_norm_eps
            _lowerCAmelCase    :							List[Any]   			=       position_embedding_type
            _lowerCAmelCase    :							str   			=       use_cache
            _lowerCAmelCase    :							Union[str, Any]   			=       classifier_dropout
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     @property
     def a	( self   ):

            '''simple docstring'''

            if self.task == "multiple-choice":
                   _lowerCAmelCase    :							Union[str, Any]   			=       {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   _lowerCAmelCase    :							List[Any]   			=       {0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ('input_ids', dynamic_axis),
                    ('attention_mask', dynamic_axis),
                ]   )

