'''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:
		UpperCamelCase_      	=	None

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

UpperCamelCase_      	=	{
    '''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''',
    },
}

UpperCamelCase_      	=	{
    '''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,
}

UpperCamelCase_      	=	'''▁'''




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Tuple     		= VOCAB_FILES_NAMES
						A_			:							Optional[int]     		= PRETRAINED_VOCAB_FILES_MAP
						A_			:							str     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A_			:							List[Any]     		= AlbertTokenizer




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       Union[str, Any]=None							,							UpperCamelCase_ :       List[str]=None							,							UpperCamelCase_ :       Union[str, Any]=True							,							UpperCamelCase_ :       int=True							,							UpperCamelCase_ :       Union[str, Any]=False							,							UpperCamelCase_ :       int="[CLS]"							,							UpperCamelCase_ :       Tuple="[SEP]"							,							UpperCamelCase_ :       List[str]="<unk>"							,							UpperCamelCase_ :       Optional[int]="[SEP]"							,							UpperCamelCase_ :       Optional[Any]="<pad>"							,							UpperCamelCase_ :       Optional[Any]="[CLS]"							,							UpperCamelCase_ :       List[Any]="[MASK]"							,							**UpperCamelCase_ :       List[str]							,							)     ->				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.
													SCREAMING_SNAKE_CASE__     :Optional[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__     :str						    = remove_space
													SCREAMING_SNAKE_CASE__     :List[str]						    = keep_accents
													SCREAMING_SNAKE_CASE__     :Tuple						    = vocab_file
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = False if not self.vocab_file else True




						def      __lowerCamelCase       (    self :       List[Any]							,							UpperCamelCase_ :       List[int]							,							UpperCamelCase_ :       Optional[List[int]] = None      )     ->				List[int]:
													SCREAMING_SNAKE_CASE__     :Dict						    = [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      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       List[int]							,							UpperCamelCase_ :       Optional[List[int]] = None      )     ->				List[int]:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [self.sep_token_id]
													SCREAMING_SNAKE_CASE__     :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 :       List[Any]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Optional[str] = None      )     ->				Tuple[str]:
													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__     :Dict						    = 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,)







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase_      	=	{
    '''configuration_m2m_100''': ['''M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''M2M100Config''', '''M2M100OnnxConfig'''],
    '''tokenization_m2m_100''': ['''M2M100Tokenizer'''],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''M2M100ForConditionalGeneration''',
		    '''M2M100Model''',
		    '''M2M100PreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
		from .tokenization_mam_aaa import MaMaaaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mam_aaa import (
				    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaMaaaForConditionalGeneration,
				    MaMaaaModel,
				    MaMaaaPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_      	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






