
'''simple docstring'''






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





def    UpperCAmelCase							(  UpperCAmelCase__							:							Any):
				return np.dot(UpperCAmelCase__       ,							UpperCAmelCase__)




class     __snake_case		:
					def __init__(     self,    *,
					    A = np.inf,    A = "linear",    A = 0.0,    ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= regularization
									lowerCamelCase					:			Union[str, Any]									= gamma
									if kernel == "linear":
													lowerCamelCase					:			Union[str, Any]									= 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'      )
													lowerCamelCase					:			Optional[int]									= 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:
													lowerCamelCase					:			List[str]									= F'''Unknown kernel: {kernel}'''
													raise ValueError(snake_case_      )
					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									return np.dot(snake_case_,    snake_case_      )
					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									return np.exp(-(self.gamma * norm_squared(vectora - vectora      ))      )
					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= observations
									lowerCamelCase					:			Dict									= 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

									((lowerCamelCase						)			, 						)					:			int									= np.shape(snake_case_      )

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

									lowerCamelCase					:			Tuple									= LinearConstraint(snake_case_,    0,    0      )
									lowerCamelCase					:			Tuple									= Bounds(0,    self.regularization      )

									lowerCamelCase					:			Optional[int]									= minimize(
									    snake_case_,    np.ones(snake_case_      ),    bounds=snake_case_,    constraints=[ly_contraint]      ).x
									lowerCamelCase					:			List[str]									= l_star

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




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= sum(
									    self.optimum[n]
									    * self.classes[n]
									    * self.kernel(self.observations[n],    snake_case_      )
									    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'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A			      =      {
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPTokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPFeatureExtractor']
					A			      =      ['CLIPImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CLIPModel',
					    'CLIPPreTrainedModel',
					    'CLIPTextModel',
					    'CLIPTextModelWithProjection',
					    'CLIPVisionModel',
					    'CLIPVisionModelWithProjection',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCLIPModel',
					    'TFCLIPPreTrainedModel',
					    'TFCLIPTextModel',
					    'TFCLIPVisionModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'FlaxCLIPModel',
					    'FlaxCLIPPreTrainedModel',
					    'FlaxCLIPTextModel',
					    'FlaxCLIPTextPreTrainedModel',
					    'FlaxCLIPVisionModel',
					    'FlaxCLIPVisionPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_clip import (
					    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPConfig,
					    CLIPOnnxConfig,
					    CLIPTextConfig,
					    CLIPVisionConfig,
					)
					from .processing_clip import CLIPProcessor
					from .tokenization_clip import CLIPTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_clip_fast import CLIPTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clip import CLIPFeatureExtractor
										from .image_processing_clip import CLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clip import (
										    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPModel,
										    CLIPPreTrainedModel,
										    CLIPTextModel,
										    CLIPTextModelWithProjection,
										    CLIPVisionModel,
										    CLIPVisionModelWithProjection,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_clip import (
										    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCLIPModel,
										    TFCLIPPreTrainedModel,
										    TFCLIPTextModel,
										    TFCLIPVisionModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_clip import (
										    FlaxCLIPModel,
										    FlaxCLIPPreTrainedModel,
										    FlaxCLIPTextModel,
										    FlaxCLIPTextPreTrainedModel,
										    FlaxCLIPVisionModel,
										    FlaxCLIPVisionPreTrainedModel,
										)


else:
					import sys

					A			      =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
