
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				return np.dot(__magic_name__		,    __magic_name__						)





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:Union[str, Any]		,    *,
						    lowerCAmelCase__							:float = np.inf		,    lowerCAmelCase__							:str = "linear"		,    lowerCAmelCase__							:float = 0.0		,    )  ->None:
										lowercase         =      regularization
										lowercase         =      gamma
										if kernel == "linear":
														lowercase         =      self.__linear
										elif kernel == "rbf":
														if self.gamma == 0:
																		raise ValueError("rbf kernel requires gamma"     )
														if not isinstance(self.gamma		,    (float, int)     ):
																		raise ValueError("gamma must be float or int"     )
														if not self.gamma > 0:
																		raise ValueError("gamma must be > 0"     )
														lowercase         =      self.__rbf
														# in the future, there could be a default value like in sklearn
														# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
														# previously it was 1/(n_features)
										else:
														lowercase         =      F'''Unknown kernel: {kernel}'''
														raise ValueError(lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:ndarray		,    lowerCAmelCase__							:ndarray     )  ->float:
										return np.dot(lowerCAmelCase__		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:ndarray		,    lowerCAmelCase__							:ndarray     )  ->float:
										return np.exp(-(self.gamma * norm_squared(vectora - vectora     ))     )





						def        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:list[ndarray]		,    lowerCAmelCase__							:ndarray     )  ->None:
										lowercase         =      observations
										lowercase         =      classes

										# using Wolfe's Dual to calculate w.
										# Primal problem: minimize 1/2*norm_squared(w)
										#   constraint: yn(w . xn + b) >= 1
										#
										# With l a vector
										# Dual problem: maximize sum_n(ln) -
										#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
										#   constraint: self.C >= ln >= 0
										#           and sum_n(ln*yn) = 0
										# Then we get w using w = sum_n(ln*yn*xn)
										# At the end we can get b ~= mean(yn - w . xn)
										#
										# Since we use kernels, we only need l_star to calculate b
										# and to classify observations

										((lowercase)     ,	)         =      np.shape(lowerCAmelCase__     )

										def to_minimize(lowerCAmelCase__							:ndarray     ) -> float:
														lowercase         =      0
														((lowercase)     ,	)         =      np.shape(lowerCAmelCase__     )
														for i in range(lowerCAmelCase__     ):
																		for j in range(lowerCAmelCase__     ):
																						s += (
																						    candidate[i]
																						    * candidate[j]
																						    * classes[i]
																						    * classes[j]
																						    * self.kernel(observations[i]		,    observations[j]     )
																						)
														return 1 / 2 * s - sum(lowerCAmelCase__     )

										lowercase         =      LinearConstraint(lowerCAmelCase__		,    0		,    0     )
										lowercase         =      Bounds(0		,    self.regularization     )

										lowercase         =      minimize(
										    lowerCAmelCase__		,    np.ones(lowerCAmelCase__     )		,    bounds=lowerCAmelCase__		,    constraints=[ly_contraint]     ).x
										lowercase         =      l_star

										# calculating mean offset of separation plane to points
										lowercase         =      0
										for i in range(lowerCAmelCase__     ):
														for j in range(lowerCAmelCase__     ):
																		s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
																		    observations[i]		,    observations[j]     )
										lowercase         =      s / n





						def        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:ndarray     )  ->int:
										lowercase         =      sum(
										    self.optimum[n]
										    * self.classes[n]
										    * self.kernel(self.observations[n]		,    lowerCAmelCase__     )
										    for n in range(len(self.classes     )     )     )
										return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()


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_barthez import BarthezTokenizer
else:
						_snake_case      :							Any     	=    None

_snake_case      :							Union[str, Any]     	=    logging.get_logger(__name__)

_snake_case      :							Optional[Any]     	=    {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_snake_case      :							int     	=    {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"
        ),
    },
}

_snake_case      :							str     	=    {
    "moussaKam/mbarthez": 10_24,
    "moussaKam/barthez": 10_24,
    "moussaKam/barthez-orangesum-title": 10_24,
}

_snake_case      :							Optional[int]     	=    "▁"





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					VOCAB_FILES_NAMES
						UpperCamelCase      :  Any				   =					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      :  Union[str, Any]				   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      :  Dict				   =					['''input_ids''', '''attention_mask''']
						UpperCamelCase      :  Optional[Any]				   =					BarthezTokenizer





						def __init__(			self							:str		,    lowerCAmelCase__							:str=None		,    lowerCAmelCase__							:List[str]=None		,    lowerCAmelCase__							:Union[str, Any]="<s>"		,    lowerCAmelCase__							:Optional[Any]="</s>"		,    lowerCAmelCase__							:str="</s>"		,    lowerCAmelCase__							:List[str]="<s>"		,    lowerCAmelCase__							:Dict="<unk>"		,    lowerCAmelCase__							:int="<pad>"		,    lowerCAmelCase__							:Optional[Any]="<mask>"		,    **lowerCAmelCase__							:Optional[int]		,    )  ->int:
										# Mask token behave like a normal word, i.e. include the space before it
										lowercase         =      AddedToken(lowerCAmelCase__		,    lstrip=lowerCAmelCase__		,    rstrip=lowerCAmelCase__     ) if isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ) else mask_token

										super().__init__(
										    lowerCAmelCase__		,    tokenizer_file=lowerCAmelCase__		,    bos_token=lowerCAmelCase__		,    eos_token=lowerCAmelCase__		,    unk_token=lowerCAmelCase__		,    sep_token=lowerCAmelCase__		,    cls_token=lowerCAmelCase__		,    pad_token=lowerCAmelCase__		,    mask_token=lowerCAmelCase__		,    **lowerCAmelCase__		,    )

										lowercase         =      vocab_file
										lowercase         =      False if not self.vocab_file else True





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:

										if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										lowercase         =      [self.cls_token_id]
										lowercase         =      [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:
										lowercase         =      [self.sep_token_id]
										lowercase         =      [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 + sep + token_ids_a + sep     ) * [0]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[str] = None     )  ->Tuple[str]:
										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(lowerCAmelCase__     ):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
														return
										lowercase         =      os.path.join(
										    lowerCAmelCase__		,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

										return (out_vocab_file,)
