'''simple docstring'''





import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def 						__A							(     UpperCAmelCase					)     ->			float:




						'''simple docstring'''
						return np.dot(UpperCAmelCase     ,UpperCAmelCase					)




class 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''





				def __init__(						self					:						List[str]       ,		*,
				    lowercase__					:						float = np.inf       ,		lowercase__					:						str = "linear"       ,		lowercase__					:						float = 0.0       ,		)							->None:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						regularization
										_UpperCamelCase			:			str   			=						gamma
										if kernel == "linear":
																_UpperCamelCase			:			Optional[int]   			=						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"					)
																_UpperCamelCase			:			Tuple   			=						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:
																_UpperCamelCase			:			Optional[Any]   			=						f'''Unknown kernel: {kernel}'''
																raise ValueError(lowercase__					)





				def  snake_case__				(						self					:						str       ,		lowercase__					:						ndarray       ,		lowercase__					:						ndarray					)							->float:


										'''simple docstring'''




										return np.dot(lowercase__       ,		lowercase__					)





				def  snake_case__				(						self					:						Any       ,		lowercase__					:						ndarray       ,		lowercase__					:						ndarray					)							->float:


										'''simple docstring'''




										return np.exp(-(self.gamma * norm_squared(vectora - vectora					))					)





				def  snake_case__				(						self					:						Union[str, Any]       ,		lowercase__					:						list[ndarray]       ,		lowercase__					:						ndarray					)							->None:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						observations
										_UpperCamelCase			:			List[Any]   			=						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

										((_UpperCamelCase)			,       )			:			List[str]   			=						np.shape(lowercase__					)

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

										_UpperCamelCase			:			Tuple   			=						LinearConstraint(lowercase__       ,		0       ,		0					)
										_UpperCamelCase			:			int   			=						Bounds(0       ,		self.regularization					)

										_UpperCamelCase			:			Union[str, Any]   			=						minimize(
										    lowercase__       ,		np.ones(lowercase__					)       ,		bounds=lowercase__       ,		constraints=[ly_contraint]					).x
										_UpperCamelCase			:			Dict   			=						l_star

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







				def  snake_case__				(						self					:						Any       ,		lowercase__					:						ndarray					)							->int:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						sum(
										    self.optimum[n]
										    * self.classes[n]
										    * self.kernel(self.observations[n]       ,		lowercase__					)
										    for n in range(len(self.classes					)					)					)
										return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()



'''simple docstring'''





import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
			import jieba

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


lowerCAmelCase_	:		int			=     logging.get_logger(__name__)

lowerCAmelCase_	:		List[str]			=     {"""vocab_file""": """vocab.txt"""}

lowerCAmelCase_	:		Optional[Any]			=     {
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

lowerCAmelCase_	:		int			=     {
    """openbmb/cpm-ant-10b""": 1024,
}
def 						__A							(     UpperCAmelCase					)     ->			List[str]:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[int]   			=						collections.OrderedDict()
						with open(UpperCAmelCase     ,"r"     ,encoding="utf-8"					) as reader:
												_UpperCamelCase			:			int   			=						reader.readlines()
						for index, token in enumerate(UpperCAmelCase					):
												_UpperCamelCase			:			Optional[int]   			=						token.rstrip("\n"					)
												_UpperCamelCase			:			List[str]   			=						index
						return vocab




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Dict       ,		lowercase__					:						List[str]       ,		lowercase__					:						Any="<unk>"       ,		lowercase__					:						str=200					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						vocab
										_UpperCamelCase			:			str   			=						unk_token
										_UpperCamelCase			:			List[Any]   			=						max_input_chars_per_word







				def  snake_case__				(						self					:						Union[str, Any]       ,		lowercase__					:						Any					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						list(lowercase__					)
										if len(lowercase__					) > self.max_input_chars_per_word:
																return [self.unk_token]

										_UpperCamelCase			:			int   			=						0
										_UpperCamelCase			:			List[str]   			=						[]
										while start < len(lowercase__					):
																_UpperCamelCase			:			Union[str, Any]   			=						len(lowercase__					)
																_UpperCamelCase			:			Union[str, Any]   			=						None
																while start < end:
																						_UpperCamelCase			:			Dict   			=						"".join(chars[start:end]					)
																						if substr in self.vocab:
																												_UpperCamelCase			:			int   			=						substr
																												break
																						end -= 1
																if cur_substr is None:
																						sub_tokens.append(self.unk_token					)
																						start += 1
																else:
																						sub_tokens.append(lowercase__					)
																						_UpperCamelCase			:			List[str]   			=						end

										return sub_tokens





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							VOCAB_FILES_NAMES
				UpperCAmelCase__							=							PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase__							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase__							=							['''input_ids''', '''attention_mask''']
				UpperCAmelCase__							=							False





				def __init__(						self					:						int       ,		lowercase__					:						str       ,		lowercase__					:						int="<d>"       ,		lowercase__					:						Tuple="</d>"       ,		lowercase__					:						Optional[int]="<s>"       ,		lowercase__					:						Optional[int]="</s>"       ,		lowercase__					:						List[str]="<pad>"       ,		lowercase__					:						Optional[int]="<unk>"       ,		lowercase__					:						int="</n>"       ,		lowercase__					:						Optional[Any]="</_>"       ,		lowercase__					:						int="left"       ,		**lowercase__					:						List[str]       ,		)							->Union[str, Any]:


										'''simple docstring'''




										requires_backends(self       ,		["jieba"]					)
										super().__init__(
										    bod_token=lowercase__       ,		eod_token=lowercase__       ,		bos_token=lowercase__       ,		eos_token=lowercase__       ,		pad_token=lowercase__       ,		unk_token=lowercase__       ,		line_token=lowercase__       ,		space_token=lowercase__       ,		padding_side=lowercase__       ,		**lowercase__       ,		)
										_UpperCamelCase			:			Union[str, Any]   			=						bod_token
										_UpperCamelCase			:			Union[str, Any]   			=						eod_token
										_UpperCamelCase			:			int   			=						load_vocab(lowercase__					)
										_UpperCamelCase			:			Any   			=						self.encoder[space_token]
										_UpperCamelCase			:			List[str]   			=						self.encoder[line_token]

										del self.encoder[space_token]
										del self.encoder[line_token]

										_UpperCamelCase			:			Dict   			=						collections.OrderedDict(sorted(self.encoder.items()       ,		key=lambda lowercase__					: x[1]					)					)
										_UpperCamelCase			:			Any   			=						{v: k for k, v in self.encoder.items()}

										_UpperCamelCase			:			Any   			=						WordpieceTokenizer(vocab=self.encoder       ,		unk_token=self.unk_token					)





				@property
				def  snake_case__				(						self					:						Tuple					)							->List[str]:


										'''simple docstring'''




										return self.encoder[self.bod_token]





				@property
				def  snake_case__				(						self					:						Tuple					)							->List[Any]:


										'''simple docstring'''




										return self.encoder[self.eod_token]





				@property
				def  snake_case__				(						self					:						List[str]					)							->int:


										'''simple docstring'''




										return self.encoder["\n"]





				@property
				def  snake_case__				(						self					:						List[str]					)							->int:


										'''simple docstring'''




										return len(self.encoder					)





				def  snake_case__				(						self					:						Dict					)							->Tuple:


										'''simple docstring'''




										return dict(self.encoder       ,		**self.added_tokens_encoder					)





				def  snake_case__				(						self					:						Optional[int]       ,		lowercase__					:						Tuple					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						[]
										for x in jieba.cut(lowercase__       ,		cut_all=lowercase__					):
																output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowercase__					)					)
										return output_tokens





				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						Any       ,		**lowercase__					:						Tuple					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[int]   			=						[i for i in token_ids if i >= 0]
										_UpperCamelCase			:			Union[str, Any]   			=						[
										    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
										]
										return super()._decode(lowercase__       ,		**lowercase__					)





				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						Tuple					)							->Union[str, Any]:


										'''simple docstring'''




										return token in self.encoder





				def  snake_case__				(						self					:						str       ,		lowercase__					:						List[str]					)							->str:


										'''simple docstring'''




										return "".join(lowercase__					)





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						Optional[Any]					)							->int:


										'''simple docstring'''




										return self.encoder.get(lowercase__       ,		self.encoder.get(self.unk_token					)					)





				def  snake_case__				(						self					:						Any       ,		lowercase__					:						Dict					)							->Dict:


										'''simple docstring'''




										return self.decoder.get(lowercase__       ,		self.unk_token					)





				def  snake_case__				(						self					:						Tuple       ,		lowercase__					:						str       ,		lowercase__					:						Optional[str] = None					)							->Tuple[str]:


										'''simple docstring'''




										if os.path.isdir(lowercase__					):
																_UpperCamelCase			:			Tuple   			=						os.path.join(
																    lowercase__       ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
										else:
																_UpperCamelCase			:			Optional[int]   			=						(filename_prefix + "-" if filename_prefix else "") + save_directory
										_UpperCamelCase			:			List[str]   			=						0
										if " " in self.encoder:
																_UpperCamelCase			:			Dict   			=						self.encoder[" "]
																del self.encoder[" "]
										if "\n" in self.encoder:
																_UpperCamelCase			:			int   			=						self.encoder["\n"]
																del self.encoder["\n"]
										_UpperCamelCase			:			List[str]   			=						collections.OrderedDict(sorted(self.encoder.items()       ,		key=lambda lowercase__					: x[1]					)					)
										with open(lowercase__       ,		"w"       ,		encoding="utf-8"					) as writer:
																for token, token_index in self.encoder.items():
																						if index != token_index:
																												logger.warning(
																												    f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																												    " Please check that the vocabulary is not corrupted!"					)
																												_UpperCamelCase			:			str   			=						token_index
																						writer.write(token + "\n"					)
																						index += 1
										return (vocab_file,)





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						List[int]       ,		lowercase__					:						List[int] = None					)							->List[int]:


										'''simple docstring'''




										if token_ids_a is None:
																return [self.bos_token_id] + token_ids_a
										return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a







				def  snake_case__				(						self					:						Optional[int]       ,		lowercase__					:						List[int]       ,		lowercase__					:						Optional[List[int]] = None       ,		lowercase__					:						bool = False					)							->List[int]:


										'''simple docstring'''




										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=lowercase__       ,		token_ids_a=lowercase__       ,		already_has_special_tokens=lowercase__					)

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


