




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




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  float:



    """simple docstring"""






    return np.dot(_lowerCAmelCase				,					_lowerCAmelCase		)






class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self,		*,
        lowerCamelCase__ = np.inf,		lowerCamelCase__ = "linear",		lowerCamelCase__ = 0.0,		):
        A			:	Optional[Any]										=       regularization
        A			:	List[str]										=       gamma
        if kernel == "linear":
            A			:	List[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"""		)
            A			:	List[Any]										=       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:
            A			:	Any										=       f'''Unknown kernel: {kernel}'''
            raise ValueError(lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__		):
        return np.dot(lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__		):
        return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	int										=       observations
        A			:	Union[str, 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

        (A							)			:	Optional[int]										=       np.shape(lowerCamelCase__		)

        def to_minimize(lowerCamelCase__		) -> float:
            A			:	List[Any]										=       0
            (A							)			:	Tuple										=       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__		)

        A			:	str										=       LinearConstraint(lowerCamelCase__,		0,		0		)
        A			:	Union[str, Any]										=       Bounds(0,		self.regularization		)

        A			:	List[str]										=       minimize(
            lowerCamelCase__,		np.ones(lowerCamelCase__		),		bounds=lowerCamelCase__,		constraints=[ly_contraint]		).x
        A			:	str										=       l_star

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






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Optional[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()







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:Any					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Any					=						{
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			List[str] =				"xlm"
    __lowerCamelCase      :			Tuple =				{
        "hidden_size": "emb_dim",
        "num_attention_heads": "n_heads",
        "num_hidden_layers": "n_layers",
        "n_words": "vocab_size",  # For backward compatibility
    }






    def __init__(	self,		lowerCamelCase__=3_0145,		lowerCamelCase__=2048,		lowerCamelCase__=12,		lowerCamelCase__=16,		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=1,		lowerCamelCase__=True,		lowerCamelCase__=512,		lowerCamelCase__=2048**-0.5,		lowerCamelCase__=1e-12,		lowerCamelCase__=0.02,		lowerCamelCase__=0,		lowerCamelCase__=1,		lowerCamelCase__=2,		lowerCamelCase__=3,		lowerCamelCase__=5,		lowerCamelCase__=True,		lowerCamelCase__="first",		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=True,		lowerCamelCase__=0.1,		lowerCamelCase__=5,		lowerCamelCase__=5,		lowerCamelCase__=0,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__=0,		**lowerCamelCase__,		):
        A			:	Dict										=       vocab_size
        A			:	int										=       emb_dim
        A			:	str										=       n_layers
        A			:	Union[str, Any]										=       n_heads
        A			:	Optional[int]										=       dropout
        A			:	Union[str, Any]										=       attention_dropout
        A			:	Optional[Any]										=       gelu_activation
        A			:	Dict										=       sinusoidal_embeddings
        A			:	int										=       causal
        A			:	Optional[Any]										=       asm
        A			:	Any										=       n_langs
        A			:	List[str]										=       use_lang_emb
        A			:	Union[str, Any]										=       layer_norm_eps
        A			:	str										=       bos_index
        A			:	int										=       eos_index
        A			:	Tuple										=       pad_index
        A			:	str										=       unk_index
        A			:	Optional[Any]										=       mask_index
        A			:	Union[str, Any]										=       is_encoder
        A			:	Tuple										=       max_position_embeddings
        A			:	List[str]										=       embed_init_std
        A			:	Tuple										=       init_std
        A			:	Tuple										=       summary_type
        A			:	int										=       summary_use_proj
        A			:	List[Any]										=       summary_activation
        A			:	Optional[Any]										=       summary_proj_to_labels
        A			:	Optional[Any]										=       summary_first_dropout
        A			:	Optional[int]										=       start_n_top
        A			:	Optional[Any]										=       end_n_top
        A			:	List[str]										=       mask_token_id
        A			:	Tuple										=       lang_id

        if "n_words" in kwargs:
            A			:	List[str]										=       kwargs["""n_words"""]

        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		**lowerCamelCase__		)






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @property
    def       _lowerCAmelCase (	self		):
        if self.task == "multiple-choice":
            A			:	Any										=       {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A			:	Union[str, Any]										=       {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]		)


