

'''simple docstring'''




import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def       A__	(							A  :     List[str]):


			'''simple docstring'''


			return np.dot(A							,       A)

class     UpperCAmelCase_ :



	"""simple docstring"""





	def __init__(    self				,			*,
	    lowerCamelCase = np.inf				,			lowerCamelCase = "linear"				,			lowerCamelCase = 0.0				,			)							->     None:




				'''simple docstring'''






				UpperCamelCase				:	str    						=					regularization
				UpperCamelCase				:	Dict    						=					gamma
				if kernel == "linear":
							UpperCamelCase				:	str    						=					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				:	str    						=					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				:	List[str]    						=					f'''Unknown kernel: {kernel}'''
							raise ValueError(lowerCAmelCase__   )

	def 	SCREAMING_SNAKE_CASE__							(    self				,			lowerCamelCase				,			lowerCamelCase   )							->     float:




				'''simple docstring'''






				return np.dot(lowerCAmelCase__				,			lowerCAmelCase__   )

	def 	SCREAMING_SNAKE_CASE__							(    self				,			lowerCamelCase				,			lowerCamelCase   )							->     float:




				'''simple docstring'''






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

	def 	SCREAMING_SNAKE_CASE__							(    self				,			lowerCamelCase				,			lowerCamelCase   )							->     None:




				'''simple docstring'''






				UpperCamelCase				:	Tuple    						=					observations
				UpperCamelCase				:	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	)						,   	)				:	Any    						=					np.shape(lowerCAmelCase__   )

				def to_minimize(lowerCamelCase   ) -> float:
							UpperCamelCase				:	Any    						=					0
							((UpperCamelCase	)						,   	)				:	Optional[Any]    						=					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__   )

				UpperCamelCase				:	int    						=					LinearConstraint(lowerCAmelCase__				,			0				,			0   )
				UpperCamelCase				:	Optional[Any]    						=					Bounds(0				,			self.regularization   )

				UpperCamelCase				:	Tuple    						=					minimize(
				    lowerCAmelCase__				,			np.ones(lowerCAmelCase__   )				,			bounds=lowerCAmelCase__				,			constraints=[ly_contraint]   ).x
				UpperCamelCase				:	Optional[Any]    						=					l_star

				# calculating mean offset of separation plane to points
				UpperCamelCase				:	List[str]    						=					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]   )
				UpperCamelCase				:	str    						=					s / n



	def 	SCREAMING_SNAKE_CASE__							(    self				,			lowerCamelCase   )							->     int:




				'''simple docstring'''






				UpperCamelCase				:	int    						=					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()


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
						import torch


_snake_case      :							Optional[Any]     	=    logging.get_logger(__name__)





@dataclass
class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:str		,    lowerCAmelCase__							:Any=False		,    lowerCAmelCase__							:Tuple=False		,    lowerCAmelCase__							:List[str]=6.0		,    lowerCAmelCase__							:int=None		,    lowerCAmelCase__							:Optional[Any]=False		,    lowerCAmelCase__							:List[Any]=False		,    lowerCAmelCase__							:int=None		,    lowerCAmelCase__							:Tuple="fp4"		,    lowerCAmelCase__							:Union[str, Any]=False		,    **lowerCAmelCase__							:Optional[Any]		,    )  ->Any:
										lowercase         =      load_in_abit
										lowercase         =      load_in_abit
										lowercase         =      llm_inta_threshold
										lowercase         =      llm_inta_skip_modules
										lowercase         =      llm_inta_enable_fpaa_cpu_offload
										lowercase         =      llm_inta_has_fpaa_weight
										lowercase         =      bnb_abit_quant_type
										lowercase         =      bnb_abit_use_double_quant

										if bnb_abit_compute_dtype is None:
														lowercase         =      torch.floataa
										elif isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
														lowercase         =      getattr(lowerCAmelCase__		,    lowerCAmelCase__     )
										elif isinstance(lowerCAmelCase__		,    torch.dtype     ):
														lowercase         =      bnb_abit_compute_dtype
										else:
														raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype"     )

										self.post_init()





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										if not isinstance(self.llm_inta_threshold		,    lowerCAmelCase__     ):
														raise ValueError("llm_int8_threshold must be a float"     )

										if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules		,    lowerCAmelCase__     ):
														raise ValueError("llm_int8_skip_modules must be a list of strings"     )
										if not isinstance(self.llm_inta_enable_fpaa_cpu_offload		,    lowerCAmelCase__     ):
														raise ValueError("llm_int8_enable_fp32_cpu_offload must be a boolean"     )

										if not isinstance(self.llm_inta_has_fpaa_weight		,    lowerCAmelCase__     ):
														raise ValueError("llm_int8_has_fp16_weight must be a boolean"     )

										if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype		,    torch.dtype     ):
														raise ValueError("bnb_4bit_compute_dtype must be torch.dtype"     )

										if not isinstance(self.bnb_abit_quant_type		,    lowerCAmelCase__     ):
														raise ValueError("bnb_4bit_quant_type must be a string"     )

										if not isinstance(self.bnb_abit_use_double_quant		,    lowerCAmelCase__     ):
														raise ValueError("bnb_4bit_use_double_quant must be a boolean"     )

										if self.load_in_abit and not version.parse(importlib.metadata.version("bitsandbytes"     )     ) >= version.parse(
										    "0.39.0"     ):
														raise ValueError(
														    "4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Tuple:
										return self.load_in_abit or self.load_in_abit





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->int:
										if self.load_in_abit:
														return "llm_int8"
										elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
														return "fp4"
										elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
														return "nf4"
										else:
														return None





						@classmethod
						def        SCREAMING_SNAKE_CASE(			cls							:Union[str, Any]		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Union[str, Any]		,    **lowerCAmelCase__							:Any     )  ->Optional[Any]:
										lowercase         =      cls(**lowerCAmelCase__     )

										lowercase         =      []
										for key, value in kwargs.items():
														if hasattr(lowerCAmelCase__		,    lowerCAmelCase__     ):
																		setattr(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
																		to_remove.append(lowerCAmelCase__     )
										for key in to_remove:
														kwargs.pop(lowerCAmelCase__		,    lowerCAmelCase__     )

										if return_unused_kwargs:
														return config, kwargs
										else:
														return config





						def        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:Union[str, os.PathLike]     )  ->Optional[int]:
										with open(lowerCAmelCase__		,    "w"		,    encoding="utf-8"     ) as writer:
														lowercase         =      self.to_dict()
														lowercase         =      json.dumps(lowerCAmelCase__		,    indent=2		,    sort_keys=lowerCAmelCase__     ) + "\n"

														writer.write(lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Dict[str, Any]:
										lowercase         =      copy.deepcopy(self.__dict__     )
										lowercase         =      str(output["bnb_4bit_compute_dtype"]     ).split("."     )[1]

										return output





						def __repr__(			self							:int     )  ->Optional[Any]:
										return F'''{self.__class__.__name__} {self.to_json_string()}'''





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:bool = True     )  ->str:
										if use_diff is True:
														lowercase         =      self.to_diff_dict()
										else:
														lowercase         =      self.to_dict()
										return json.dumps(lowerCAmelCase__		,    indent=2		,    sort_keys=lowerCAmelCase__     ) + "\n"





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Dict[str, Any]:
										lowercase         =      self.to_dict()

										# get the default config dict
										lowercase         =      BitsAndBytesConfig().to_dict()

										lowercase         =      {}

										# only serialize values that differ from the default config
										for key, value in config_dict.items():
														if value != default_config_dict[key]:
																		lowercase         =      value

										return serializable_config_dict
