





"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__       :					Any						=			logging.get_logger(__name__)

__magic_name__       :					List[Any]						=			{"""vocab_file""": """sentencepiece.model"""}

__magic_name__       :					Dict						=			{
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

__magic_name__       :					List[Any]						=			{
    """google/rembert""": 2_5_6,
}






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				List[Any]										=  VOCAB_FILES_NAMES
			__lowerCAmelCase     :				Optional[int]										=  PRETRAINED_VOCAB_FILES_MAP
			__lowerCAmelCase     :				Tuple										=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







			def __init__(      self						,   _A						,   _A=False						,   _A=True						,   _A=True						,   _A="[CLS]"						,   _A="[SEP]"						,   _A="[UNK]"						,   _A="[SEP]"						,   _A="[PAD]"						,   _A="[CLS]"						,   _A="[MASK]"						,   **_A						,   ):






									'''simple docstring'''







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

									UpperCamelCase				:		Dict				   =			do_lower_case
									UpperCamelCase				:		List[str]				   =			remove_space
									UpperCamelCase				:		str				   =			keep_accents
									UpperCamelCase				:		Union[str, Any]				   =			vocab_file

									UpperCamelCase				:		Any				   =			spm.SentencePieceProcessor()
									self.sp_model.Load(_A    )







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return len(self.sp_model    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			{self.convert_ids_to_tokens(_A    ): i for i in range(self.vocab_size    )}
									vocab.update(self.added_tokens_encoder    )
									return vocab







			def __getstate__(      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			self.__dict__.copy()
									UpperCamelCase				:		Union[str, Any]				   =			None
									return state







			def __setstate__(      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			d
									UpperCamelCase				:		int				   =			spm.SentencePieceProcessor()
									self.sp_model.Load(self.vocab_file    )







			def   _a      (      self						,   _A						,   _A=False    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			self.sp_model.EncodeAsPieces(_A    )
									return pieces







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									return self.sp_model.PieceToId(_A    )







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									return self.sp_model.IdToPiece(_A    )







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			self.sp_model.decode_pieces(_A    )
									return out_string







			def   _a      (      self						,   _A						,   _A = None    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			[self.sep_token_id]
									UpperCamelCase				:		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   _a      (      self						,   _A						,   _A = None						,   _A = False    ):






									'''simple docstring'''







									if already_has_special_tokens:
															if token_ids_a is not None:
																					raise ValueError(
																					    """You should not supply a second sequence if the provided sequence of """
																					    """ids is already formatted with special tokens for the model."""    )
															return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

									if token_ids_a is not None:
															return [1] + ([0] * len(_A    )) + [1] + ([0] * len(_A    )) + [1]
									return [1] + ([0] * len(_A    )) + [1]







			def   _a      (      self						,   _A						,   _A = None    ):






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			[self.sep_token_id]
									UpperCamelCase				:		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   _a      (      self						,   _A						,   _A = None    ):






									'''simple docstring'''







									if not os.path.isdir(_A    ):
															logger.error("""Vocabulary path ({}) should be a directory""".format(_A    )    )
															return
									UpperCamelCase				:		str				   =			os.path.join(
									    _A						,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

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

									return (out_vocab_file,)










"""simple docstring"""


import colorsys

from PIL import Image  # type: ignore

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[int]				   =			x
						UpperCamelCase				:		str				   =			y
						for step in range(SCREAMING_SNAKE_CASE     ):  # noqa: B007
												UpperCamelCase				:		str				   =			a * a - b * b + x
												UpperCamelCase				:		Any				   =			2 * a * b + y
												UpperCamelCase				:		List[str]				   =			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							(SCREAMING_SNAKE_CASE     ):
						if distance == 1:
												return (0, 0, 0)
						else:
												return (255, 255, 255)

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 255     ) for i in colorsys.hsv_to_rgb(SCREAMING_SNAKE_CASE							,     1							,     1     )     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE = 800							,     SCREAMING_SNAKE_CASE = 600							,     SCREAMING_SNAKE_CASE = -0.6							,     SCREAMING_SNAKE_CASE = 0							,     SCREAMING_SNAKE_CASE = 3.2							,     SCREAMING_SNAKE_CASE = 50							,     SCREAMING_SNAKE_CASE = True							,     ):
						UpperCamelCase				:		Tuple				   =			Image.new("""RGB"""							,     (image_width, image_height)     )
						UpperCamelCase				:		Dict				   =			img.load()

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

																		UpperCamelCase				:		Union[str, Any]				   =			get_distance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								UpperCamelCase				:		int				   =			get_color_coded_rgb(SCREAMING_SNAKE_CASE     )
																		else:
																								UpperCamelCase				:		Optional[int]				   =			get_black_and_white_rgb(SCREAMING_SNAKE_CASE     )

						return img


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# colored version, full figure
		__magic_name__       :					int						=			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()



