





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 :    List[Any]  							= None

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

_lowerCamelCase :    Union[str, Any]  							= {
    '''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 :    str  							= {
    '''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,
}

_lowerCamelCase :    Optional[Any]  							= '''▁'''


class 						lowerCamelCase       (__SCREAMING_SNAKE_CASE      ):
							UpperCAmelCase_      						=   VOCAB_FILES_NAMES
							UpperCAmelCase_      						=   PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_      						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_      						=   AlbertTokenizer






							def __init__( self     :					List[str],			_UpperCAmelCase     :					List[Any]=None,			_UpperCAmelCase     :					str=None,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Tuple=True,			_UpperCAmelCase     :					List[str]=False,			_UpperCAmelCase     :					int="[CLS]",			_UpperCAmelCase     :					Optional[Any]="[SEP]",			_UpperCAmelCase     :					Tuple="<unk>",			_UpperCAmelCase     :					List[Any]="[SEP]",			_UpperCAmelCase     :					Tuple="<pad>",			_UpperCAmelCase     :					List[Any]="[CLS]",			_UpperCAmelCase     :					List[Any]="[MASK]",			**_UpperCAmelCase     :					Optional[int],			)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						(
														    AddedToken(UpperCamelCase__,			lstrip=UpperCamelCase__,			rstrip=UpperCamelCase__,			normalized=UpperCamelCase__						)
														    if isinstance(UpperCamelCase__,			UpperCamelCase__						)
														    else mask_token
														)

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

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						do_lower_case
														SCREAMING_SNAKE_CASE__       :      List[str]     =						remove_space
														SCREAMING_SNAKE_CASE__       :      Any     =						keep_accents
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						vocab_file
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						False if not self.vocab_file else True






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None						)				->			List[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						[self.sep_token_id]
														SCREAMING_SNAKE_CASE__       :      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     :					int,			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None						)				->			List[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[self.sep_token_id]
														SCREAMING_SNAKE_CASE__       :      List[str]     =						[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     :					List[Any],			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[str] = None						)				->			Tuple[str]:





														"""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(UpperCamelCase__						):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
																					return
														SCREAMING_SNAKE_CASE__       :      List[str]     =						os.path.join(
														    UpperCamelCase__,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

														return (out_vocab_file,)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase :    Union[str, Any]  							= {
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase :    int  							= [
		    '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BigBirdPegasusForCausalLM''',
		    '''BigBirdPegasusForConditionalGeneration''',
		    '''BigBirdPegasusForQuestionAnswering''',
		    '''BigBirdPegasusForSequenceClassification''',
		    '''BigBirdPegasusModel''',
		    '''BigBirdPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_bigbird_pegasus import (
		    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BigBirdPegasusConfig,
		    BigBirdPegasusOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bigbird_pegasus import (
				    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BigBirdPegasusForCausalLM,
				    BigBirdPegasusForConditionalGeneration,
				    BigBirdPegasusForQuestionAnswering,
				    BigBirdPegasusForSequenceClassification,
				    BigBirdPegasusModel,
				    BigBirdPegasusPreTrainedModel,
				)


else:
		import sys

		_lowerCamelCase :    Optional[Any]  							= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






