





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 :str     =    None

__lowerCAmelCase :Union[str, Any]     =    logging.get_logger(__name__)
__lowerCAmelCase :Union[str, Any]     =    {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase :str     =    {
    '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 :int     =    {
    '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 :Dict     =    '▁'






class        _a(    __A    ):
		lowerCamelCase__     :Optional[int]   				=		VOCAB_FILES_NAMES
		lowerCamelCase__     :List[str]   				=		PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase__     :Tuple   				=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase__     :str   				=		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 ,						)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   (
							    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 ,						)

							_snake_case       :					Optional[int]				       =   do_lower_case
							_snake_case       :					List[str]				       =   remove_space
							_snake_case       :					Dict				       =   keep_accents
							_snake_case       :					int				       =   vocab_file
							_snake_case       :					int				       =   False if not self.vocab_file else True







		def        lowercase       (    self ,						__snake_case ,						__snake_case = None				)      ->      List[int]:


							'''simple docstring'''

							_snake_case       :					int				       =   [self.sep_token_id]
							_snake_case       :					List[str]				       =   [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        lowercase       (    self ,						__snake_case ,						__snake_case = None				)      ->      List[int]:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   [self.sep_token_id]
							_snake_case       :					Optional[int]				       =   [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        lowercase       (    self ,						__snake_case ,						__snake_case = 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(__snake_case				):
												logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
												return
							_snake_case       :					Union[str, 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,)






import math

from numpy import inf
from scipy.integrate import quad







def    A				(							UpperCAmelCase				):
					if num <= 0:
										raise ValueError("math domain error"				)

					return quad(UpperCAmelCase  ,  0  ,  UpperCAmelCase  ,  args=(UpperCAmelCase)				)[0]







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return math.pow(UpperCAmelCase  ,  z - 1				) * math.exp(-x				)


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

							testmod()