

"""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:
				_lowercase   :	List[str]												=						None

_lowercase   :	Dict												=						logging.get_logger(__name__)
_lowercase   :	List[str]												=						{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_lowercase   :	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',
    },
}

_lowercase   :	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,
}

_lowercase   :	Union[str, Any]												=						'▁'
class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: Optional[Any]					=					VOCAB_FILES_NAMES
	a__		: Tuple					=					PRETRAINED_VOCAB_FILES_MAP
	a__		: Tuple					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a__		: Union[str, Any]					=					AlbertTokenizer



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

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

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



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

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

		return (out_vocab_file,)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    	=		{
    "configuration_upernet": ["UperNetConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "UperNetForSemanticSegmentation",
       "UperNetPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_upernet import UperNetConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
   import sys

   _lowercase    	=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
