

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


_lowerCAmelCase					:			Optional[int]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			List[str]  				=			{"vocab_file": "sentencepiece.model"}

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

_lowerCAmelCase					:			Optional[int]  				=			{
    "google/rembert": 256,
}






class 	__magic_name__    (			A__      ):
			SCREAMING_SNAKE_CASE							   =						VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE							   =						PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE							   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





			def __init__(		self					,  __snake_case					,  __snake_case=False					,  __snake_case=True					,  __snake_case=True					,  __snake_case="[CLS]"					,  __snake_case="[SEP]"					,  __snake_case="[UNK]"					,  __snake_case="[SEP]"					,  __snake_case="[PAD]"					,  __snake_case="[CLS]"					,  __snake_case="[MASK]"					,  **__snake_case					,  )   -> int:




						'''simple docstring'''
						super().__init__(
						    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					,  )

						__a        =do_lower_case
						__a        =remove_space
						__a        =keep_accents
						__a        =vocab_file

						__a        =spm.SentencePieceProcessor()
						self.sp_model.Load(__snake_case					)





			@property
			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						return len(self.sp_model					)





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        ={self.convert_ids_to_tokens(__snake_case					): i for i in range(self.vocab_size					)}
						vocab.update(self.added_tokens_encoder					)
						return vocab





			def __getstate__(		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =self.__dict__.copy()
						__a        =None
						return state





			def __setstate__(		self					,  __snake_case					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =d
						__a        =spm.SentencePieceProcessor()
						self.sp_model.Load(self.vocab_file					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case=False					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =self.sp_model.EncodeAsPieces(__snake_case					)
						return pieces





			def 		__magic_name__    (		self					,  __snake_case					)   -> int:




						'''simple docstring'''
						return self.sp_model.PieceToId(__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					)   -> List[Any]:




						'''simple docstring'''
						return self.sp_model.IdToPiece(__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					)   -> List[Any]:




						'''simple docstring'''
						__a        =self.sp_model.decode_pieces(__snake_case					)
						return out_string





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




						'''simple docstring'''
						__a        =[self.sep_token_id]
						__a        =[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 		__magic_name__    (		self					,  __snake_case					,  __snake_case = None					,  __snake_case = False					)   -> List[int]:




						'''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(__snake_case					)) + [1] + ([0] * len(__snake_case					)) + [1]
						return [1] + ([0] * len(__snake_case					)) + [1]





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




						'''simple docstring'''
						__a        =[self.sep_token_id]
						__a        =[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 		__magic_name__    (		self					,  __snake_case					,  __snake_case = None					)   -> Tuple[str]:




						'''simple docstring'''
						if not os.path.isdir(__snake_case					):
									logger.error('Vocabulary path ({}) should be a directory'.format(__snake_case					)					)
									return
						__a        =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,)











'''simple docstring'''






from math import isclose, sqrt


def  snake_case_     (					_lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float  )      ->   tuple[float, float, float]:
							UpperCAmelCase :       Optional[int] 							=		point_y / 4 / point_x
							UpperCAmelCase :       str 							=		2 * normal_gradient / (1 + normal_gradient * normal_gradient)
							UpperCAmelCase :       Any 							=		(1 - normal_gradient * normal_gradient) / (
							    1 + normal_gradient * normal_gradient
							)
							UpperCAmelCase :       Union[str, Any] 							=		(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

							# to find the next point, solve the simultaeneous equations:
							# y^2 + 4x^2 = 100
							# y - b = m * (x - a)
							# ==> A x^2 + B x + C = 0
							UpperCAmelCase :       Union[str, Any] 							=		outgoing_gradient**2 + 4
							UpperCAmelCase :       Dict 							=		2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
							UpperCAmelCase :       List[Any] 							=		(point_y - outgoing_gradient * point_x) ** 2 - 100

							UpperCAmelCase :       List[str] 							=		(
							    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)
							UpperCAmelCase :       Optional[int] 							=		(
							    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)

							# two solutions, one of which is our input point
							UpperCAmelCase :       Optional[Any] 							=		x_minus if isclose(_lowerCAmelCase   ,      _lowerCAmelCase  ) else x_plus
							UpperCAmelCase :       Union[str, Any] 							=		point_y + outgoing_gradient * (next_x - point_x)

							return next_x, next_y, outgoing_gradient


def  snake_case_     (					_lowerCAmelCase  :						float = 1.4   ,      _lowerCAmelCase  :						float = -9.6  )      ->   int:
							UpperCAmelCase :       int 							=		0
							UpperCAmelCase :       float 							=		first_x_coord
							UpperCAmelCase :       float 							=		first_y_coord
							UpperCAmelCase :       float 							=		(1_0.1 - point_y) / (0.0 - point_x)

							while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       str 							=		next_point(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														num_reflections += 1

							return num_reflections


if __name__ == "__main__":
						print(F"{solution() = }")





