
"""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					:	Union[str, Any]							=		None

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

a					:	Optional[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''',
    },
}

a					:	Dict							=		{
    '''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,
}

a					:	Any							=		'''▁'''




class 		a_						( _UpperCAmelCase	):
    a      :				List[str]     =				VOCAB_FILES_NAMES
    a      :				Tuple     =				PRETRAINED_VOCAB_FILES_MAP
    a      :				Optional[Any]     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a      :				Any     =				AlbertTokenizer







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






        '''simple docstring'''






        _UpperCAmelCase          =   (
            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     ,					)

        _UpperCAmelCase          =   do_lower_case
        _UpperCAmelCase          =   remove_space
        _UpperCAmelCase          =   keep_accents
        _UpperCAmelCase          =   vocab_file
        _UpperCAmelCase          =   False if not self.vocab_file else True







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       List[int]     ,					__UpperCamelCase			:       Optional[List[int]] = None     )		->List[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   [self.sep_token_id]
        _UpperCAmelCase          =   [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			:       str     ,					__UpperCamelCase			:       List[int]     ,					__UpperCamelCase			:       Optional[List[int]] = None     )		->List[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   [self.sep_token_id]
        _UpperCAmelCase          =   [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			:       Optional[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
        _UpperCAmelCase          =   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"""



import colorsys

from PIL import Image  # type: ignore


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  float:





    """simple docstring"""

    _UpperCAmelCase          =   x
    _UpperCAmelCase          =   y
    for step in range(_A      ):  # noqa: B007
        _UpperCAmelCase          =   a * a - b * b + x
        _UpperCAmelCase          =   2 * a * b + y
        _UpperCAmelCase          =   a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

    if distance == 1:
        return (0, 0, 0)
    else:
        return (2_5_5, 2_5_5, 2_5_5)


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 2_5_5      ) for i in colorsys.hsv_to_rgb(_A					,      1					,      1      )      )


def  _UpperCamelCase	(						_A = 8_0_0					,      _A = 6_0_0					,      _A = -0.6					,      _A = 0					,      _A = 3.2					,      _A = 5_0					,      _A = True					,      )    ->  Image.Image:





    """simple docstring"""

    _UpperCAmelCase          =   Image.new("""RGB"""					,      (image_width, image_height)      )
    _UpperCAmelCase          =   img.load()

    # loop through the image-coordinates
    for image_x in range(_A      ):
        for image_y in range(_A      ):
            # determine the figure-coordinates based on the image-coordinates
            _UpperCAmelCase          =   figure_width / image_width * image_height
            _UpperCAmelCase          =   figure_center_x + (image_x / image_width - 0.5) * figure_width
            _UpperCAmelCase          =   figure_center_y + (image_y / image_height - 0.5) * figure_height

            _UpperCAmelCase          =   get_distance(_A					,      _A					,      _A      )

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                _UpperCAmelCase          =   get_color_coded_rgb(_A      )
            else:
                _UpperCAmelCase          =   get_black_and_white_rgb(_A      )

    return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 a					:	List[str]							=		get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()