





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_		:				Optional[int]				=						None

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

lowercase_		:				Dict				=						{
    '''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_		:				int				=						{
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

lowercase_		:				Dict				=						'''▁'''


class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	List[str]								=	VOCAB_FILES_NAMES
						_UpperCamelCase  :	Tuple								=	PRETRAINED_VOCAB_FILES_MAP
						_UpperCamelCase  :	List[Any]								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCamelCase  :	Union[str, Any]								=	AlbertTokenizer







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





										'''simple docstring'''







										_snake_case :			List[Any]  						=   (
										    AddedToken(lowerCamelCase_ ,				lstrip=lowerCamelCase_ ,				rstrip=lowerCamelCase_ ,				normalized=lowerCamelCase_					)
										    if isinstance(lowerCamelCase_ ,				lowerCamelCase_					)
										    else mask_token
										)

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

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







						def 		__UpperCAmelCase       (    self			:		Tuple ,				lowerCamelCase_			:		List[int] ,				lowerCamelCase_			:		Optional[List[int]] = None					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   [self.sep_token_id]
										_snake_case :			Union[str, 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 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		List[int] ,				lowerCamelCase_			:		Optional[List[int]] = None					):





										'''simple docstring'''







										_snake_case :			Dict  						=   [self.sep_token_id]
										_snake_case :			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 		__UpperCAmelCase       (    self			:		str ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		Optional[str] = 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(lowerCamelCase_					):
														logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
														return
										_snake_case :			Tuple  						=   os.path.join(
										    lowerCamelCase_ ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

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

										return (out_vocab_file,)













from math import factorial

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				if successes > trials:
								raise ValueError('successes must be lower or equal to trials' )
				if trials < 0 or successes < 0:
								raise ValueError('the function is defined for non-negative integers' )
				if not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ) or not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ):
								raise ValueError('the function is defined for non-negative integers' )
				if not 0 < prob < 1:
								raise ValueError('prob has to be in range of 1 - 0' )
				_snake_case :			Optional[int]  						=   (prob**successes) * ((1 - prob) ** (trials - successes))
				# Calculate the binomial coefficient: n! / k!(n-k)!
				_snake_case :			List[Any]  						=   float(factorial(__lowerCAmelCase ) )
				coefficient /= factorial(__lowerCAmelCase ) * factorial(trials - successes )
				return probability * coefficient


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print('''Probability of 2 successes out of 4 trails''')
		print('''with probability of 0.75 is:''', end=''' ''')
		print(binomial_distribution(2, 4, 0.75))






