



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







def 	lowerCamelCase_   (  _a    :		ndarray  ):



    '''simple docstring'''




    return np.dot(__lowerCAmelCase					,						__lowerCAmelCase  )







class 	_snake_case							:






   '''simple docstring'''






   def __init__(  self:			List[str]   ,*,
       lowerCamelCase_:			int = np.inf   ,lowerCamelCase_:			List[Any] = "linear"   ,lowerCamelCase_:			str = 0.0   ,)     ->	None:
       UpperCAmelCase_			:    Dict   						= regularization
       UpperCAmelCase_			:    int   						= gamma
       if kernel == "linear":
           UpperCAmelCase_			:    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"""				)
           UpperCAmelCase_			:    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:
           UpperCAmelCase_			:    str   						= F'''Unknown kernel: {kernel}'''
           raise ValueError(_UpperCamelCase				)


   def 	A__  (  self:			Any   ,lowerCamelCase_:			Dict   ,lowerCamelCase_:			Optional[Any]				)     ->	float:
       return np.dot(_UpperCamelCase   ,_UpperCamelCase				)


   def 	A__  (  self:			str   ,lowerCamelCase_:			Optional[int]   ,lowerCamelCase_:			List[Any]				)     ->	float:
       return np.exp(-(self.gamma * norm_squared(vectora - vectora				))				)


   def 	A__  (  self:			str   ,lowerCamelCase_:			Union[str, Any]   ,lowerCamelCase_:			Optional[int]				)     ->	None:
       UpperCAmelCase_			:    str   						= observations
       UpperCAmelCase_			:    str   						= 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_  )						,        )			:    Tuple   						= np.shape(_UpperCamelCase				)

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

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

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

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






   def 	A__  (  self:			Tuple   ,lowerCamelCase_:			Tuple				)     ->	int:
       UpperCAmelCase_			:    Optional[Any]   						= sum(
           self.optimum[n]
           * self.classes[n]
           * self.kernel(self.observations[n]   ,_UpperCamelCase				)
           for n in range(len(self.classes				)				)				)
       return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
      import doctest

      doctest.testmod()


import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	_lowerCAmelCase					(	__a					,      unittest.TestCase       ):
				_lowercase					=CLIPTokenizer
				_lowercase					=CLIPTokenizerFast
				_lowercase					=True
				_lowercase					={}
				_lowercase					=False




				def      __a						(					self     )				->      Dict:
							super().setUp()

							# fmt: off
							lowerCAmelCase_						=	["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
							# fmt: on
							lowerCAmelCase_						=	dict(zip(_UpperCamelCase				,				range(len(_UpperCamelCase     )     )     )     )
							lowerCAmelCase_						=	["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
							lowerCAmelCase_						=	{"unk_token": "<unk>"}

							lowerCAmelCase_						=	os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES["vocab_file"]     )
							lowerCAmelCase_						=	os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES["merges_file"]     )
							with open(self.vocab_file				,				"w"				,				encoding="utf-8"     ) as fp:
										fp.write(json.dumps(_UpperCamelCase     ) + "\n"     )
							with open(self.merges_file				,				"w"				,				encoding="utf-8"     ) as fp:
										fp.write("\n".join(_UpperCamelCase     )     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      Any:
							kwargs.update(self.special_tokens_map     )
							return CLIPTokenizer.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      int:
							kwargs.update(self.special_tokens_map     )
							return CLIPTokenizerFast.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase     )				->      List[str]:
							lowerCAmelCase_						=	"lower newer"
							lowerCAmelCase_						=	"lower newer"
							return input_text, output_text




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	CLIPTokenizer(self.vocab_file				,				self.merges_file				,				**self.special_tokens_map     )
							lowerCAmelCase_						=	"lower newer"
							lowerCAmelCase_						=	["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
							lowerCAmelCase_						=	tokenizer.tokenize(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

							lowerCAmelCase_						=	tokens + [tokenizer.unk_token]
							lowerCAmelCase_						=	[10, 2, 16, 9, 3, 2, 16, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase     )				,				_UpperCamelCase     )




				@require_ftfy
				def      __a						(					self     )				->      Union[str, Any]:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
													lowerCAmelCase_						=	self.tokenizer_class.from_pretrained(_UpperCamelCase				,				**_UpperCamelCase     )
													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(_UpperCamelCase				,				**_UpperCamelCase     )

													lowerCAmelCase_						=	"A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
													lowerCAmelCase_						=	tokenizer_s.tokenize(_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer_r.tokenize(_UpperCamelCase     )

													self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

													# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
													# with Tilde) encoded in 2 different ways
													lowerCAmelCase_						=	"xa\u0303y" + " " + "x\xe3y"
													lowerCAmelCase_						=	tokenizer_s.tokenize(_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer_r.tokenize(_UpperCamelCase     )

													self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

													# Test that the tokenization is identical on unicode of space type
													lowerCAmelCase_						=	[
													    "\u0009",  # (horizontal tab, '\t')
													    "\u000B",  # (vertical tab)
													    "\u000C",  # (form feed)
													    "\u0020",  # (space, ' ')
													    "\u200E",  # (left-to-right mark):w
													    "\u200F",  # (right-to-left mark)
													]
													for unicode_seq in spaces_unicodes:
																lowerCAmelCase_						=	tokenizer_s.tokenize(_UpperCamelCase     )
																lowerCAmelCase_						=	tokenizer_r.tokenize(_UpperCamelCase     )

																self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

													# Test that the tokenization is identical on unicode of line break type
													lowerCAmelCase_						=	[
													    "\u000A",  # (line feed, '\n')
													    "\r\n",  # (carriage return and line feed, '\r\n')
													    "\u000D",  # (carriage return, '\r')
													    "\r",  # (carriage return, '\r')
													    "\u000D",  # (carriage return, '\r')
													    "\u2028",  # (line separator)
													    "\u2029",  # (paragraph separator)
													    # "\u0085", # (next line)
													]

													# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
													# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
													# space (and thus into an empty list).

													for unicode_seq in line_break_unicodes:
																lowerCAmelCase_						=	tokenizer_s.tokenize(_UpperCamelCase     )
																lowerCAmelCase_						=	tokenizer_r.tokenize(_UpperCamelCase     )

																self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self     )				->      str:
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
													lowerCAmelCase_						=	"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
													lowerCAmelCase_						=	f"""{text_of_1_token} {text_of_1_token}"""

													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(
													    _UpperCamelCase				,				use_fast=_UpperCamelCase				,				)
													lowerCAmelCase_						=	tokenizer_r(_UpperCamelCase				,				return_offsets_mapping=_UpperCamelCase				,				add_special_tokens=_UpperCamelCase     )
													self.assertEqual(encoding.offset_mapping[0]				,				(0, len(_UpperCamelCase     ))     )
													self.assertEqual(
													    encoding.offset_mapping[1]				,				(len(_UpperCamelCase     ) + 1, len(_UpperCamelCase     ) + 1 + len(_UpperCamelCase     ))				,				)

													lowerCAmelCase_						=	f""" {text}"""

													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(
													    _UpperCamelCase				,				use_fast=_UpperCamelCase				,				)
													lowerCAmelCase_						=	tokenizer_r(_UpperCamelCase				,				return_offsets_mapping=_UpperCamelCase				,				add_special_tokens=_UpperCamelCase     )
													self.assertEqual(encoding.offset_mapping[0]				,				(1, 1 + len(_UpperCamelCase     ))     )
													self.assertEqual(
													    encoding.offset_mapping[1]				,				(1 + len(_UpperCamelCase     ) + 1, 1 + len(_UpperCamelCase     ) + 1 + len(_UpperCamelCase     ))				,				)




				def      __a						(					self     )				->      Optional[Any]:
							# Test related to the breaking change introduced in transformers v4.17.0
							# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
							with self.assertRaises(_UpperCamelCase     ) as context:
										self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer"     )

							self.assertTrue(
							    context.exception.args[0].startswith(
							        "The `backend_tokenizer` provided does not match the expected format."     )     )




				@require_ftfy
				def      __a						(					self     )				->      str:
							super().test_tokenization_python_rust_equals()




				def      __a						(					self     )				->      Any:
							# CLIP always lower cases letters
							pass



