
'''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:
  a_											=						None

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

a_											=						{
    "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",
    },
}

a_											=						{
    "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,
}

a_											=						"▁"






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						VOCAB_FILES_NAMES
 UpperCAmelCase_		    =						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_		    =						AlbertTokenizer





 def __init__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=True ,       lowercase_=True ,       lowercase_=False ,       lowercase_="[CLS]" ,       lowercase_="[SEP]" ,       lowercase_="<unk>" ,       lowercase_="[SEP]" ,       lowercase_="<pad>" ,       lowercase_="[CLS]" ,       lowercase_="[MASK]" ,       **lowercase_ ,       ):
     # 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.
     snake_case_ :    Union[str, Any]     			=						(
         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_ ,       )

     snake_case_ :    Optional[Any]     			=						do_lower_case
     snake_case_ :    Tuple     			=						remove_space
     snake_case_ :    str     			=						keep_accents
     snake_case_ :    int     			=						vocab_file
     snake_case_ :    Dict     			=						False if not self.vocab_file else True





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     snake_case_ :    Optional[int]     			=						[self.sep_token_id]
     snake_case_ :    Tuple     			=						[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        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     snake_case_ :    List[Any]     			=						[self.sep_token_id]
     snake_case_ :    List[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        snake_case__		(    self ,       lowercase_ ,       lowercase_ = 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
     snake_case_ :    Any     			=						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,)






'''simple docstring'''
from __future__ import annotations

from cmath import sqrt

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if a == 0:
        raise ValueError("Coefficient 'a' must not be zero."       )
    snake_case_ :    Union[str, Any]     			=						b * b - 4 * a * c

    snake_case_ :    List[str]     			=						(-b + sqrt(__SCREAMING_SNAKE_CASE       )) / (2 * a)
    snake_case_ :    int     			=						(-b - sqrt(__SCREAMING_SNAKE_CASE       )) / (2 * a)

    return (
        root_a.real if not root_a.imag else root_a,
        root_a.real if not root_a.imag else root_a,
    )

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_			, snake_case_ :    str     			=						quadratic_roots(a=5,		b=6,		c=1       )
    print(f'The solutions are: {solutiona} and {solutiona}'       )


if __name__ == "__main__":
  main()




