


'''simple docstring'''


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


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     return np.dot(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)







class 							__magic_name__	:

     """simple docstring"""






     def __init__(   self			,     *,
         lowerCamelCase = np.inf			,     lowerCamelCase = "linear"			,     lowerCamelCase = 0.0			,     ):


          '''simple docstring'''





          __A	:       int      =							regularization
          __A	:       Any      =							gamma
          if kernel == "linear":
               __A	:       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	:       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:
               __A	:       Optional[int]      =							f"Unknown kernel: {kernel}"
               raise ValueError(lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          return np.dot(lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





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


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Any      =							observations
          __A	:       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)  ,)	:       List[Any]      =							np.shape(lowerCamelCase		)

          def to_minimize(lowerCamelCase		) -> float:
               __A	:       Optional[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	:       List[str]      =							Bounds(0			,     self.regularization		)

          __A	:       List[Any]      =							minimize(
              lowerCamelCase			,     np.ones(lowerCamelCase		)			,     bounds=lowerCamelCase			,     constraints=[ly_contraint]		).x
          __A	:       Optional[Any]      =							l_star

          # calculating mean offset of separation plane to points
          __A	:       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]		)
          __A	:       Tuple      =							s / n


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       str      =							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()





'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import AlignProcessor, EfficientNetImageProcessor







@require_vision
class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							tempfile.mkdtemp()

          __A	:       List[Any]      =							[
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          __A	:       Tuple      =							os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["vocab_file"]		)
          with open(self.vocab_file			,     "w"			,     encoding="utf-8"		) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)

          __A	:       List[str]      =							{
              "do_resize": True,
              "size": 20,
              "do_center_crop": True,
              "crop_size": 18,
              "do_normalize": True,
              "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
              "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
          }
          __A	:       Union[str, Any]      =							os.path.join(self.tmpdirname			,     lowerCamelCase		)
          with open(self.image_processor_file			,     "w"			,     encoding="utf-8"		) as fp:
               json.dump(lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          return BertTokenizer.from_pretrained(self.tmpdirname			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          return BertTokenizerFast.from_pretrained(self.tmpdirname			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          shutil.rmtree(self.tmpdirname		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							[np.random.randint(255			,     size=(3, 30, 400)			,     dtype=np.uinta		)]
          __A	:       int      =							[Image.fromarray(np.moveaxis(lowerCamelCase			,     0			,     -1		)		) for x in image_inputs]
          return image_inputs


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							self.get_tokenizer()
          __A	:       int      =							self.get_rust_tokenizer()
          __A	:       List[str]      =							self.get_image_processor()

          __A	:       List[Any]      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)
          processor_slow.save_pretrained(self.tmpdirname		)
          __A	:       List[Any]      =							AlignProcessor.from_pretrained(self.tmpdirname			,     use_fast=lowerCamelCase		)

          __A	:       int      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)
          processor_fast.save_pretrained(self.tmpdirname		)
          __A	:       List[str]      =							AlignProcessor.from_pretrained(self.tmpdirname		)

          self.assertEqual(processor_slow.tokenizer.get_vocab()			,     tokenizer_slow.get_vocab()		)
          self.assertEqual(processor_fast.tokenizer.get_vocab()			,     tokenizer_fast.get_vocab()		)
          self.assertEqual(tokenizer_slow.get_vocab()			,     tokenizer_fast.get_vocab()		)
          self.assertIsInstance(processor_slow.tokenizer			,     lowerCamelCase		)
          self.assertIsInstance(processor_fast.tokenizer			,     lowerCamelCase		)

          self.assertEqual(processor_slow.image_processor.to_json_string()			,     image_processor.to_json_string()		)
          self.assertEqual(processor_fast.image_processor.to_json_string()			,     image_processor.to_json_string()		)
          self.assertIsInstance(processor_slow.image_processor			,     lowerCamelCase		)
          self.assertIsInstance(processor_fast.image_processor			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[Any]      =							AlignProcessor(tokenizer=self.get_tokenizer()			,     image_processor=self.get_image_processor()		)
          processor.save_pretrained(self.tmpdirname		)

          __A	:       List[str]      =							self.get_tokenizer(bos_token="(BOS)"			,     eos_token="(EOS)"		)
          __A	:       int      =							self.get_image_processor(do_normalize=lowerCamelCase			,     padding_value=1.0		)

          __A	:       Optional[int]      =							AlignProcessor.from_pretrained(
              self.tmpdirname			,     bos_token="(BOS)"			,     eos_token="(EOS)"			,     do_normalize=lowerCamelCase			,     padding_value=1.0		)

          self.assertEqual(processor.tokenizer.get_vocab()			,     tokenizer_add_kwargs.get_vocab()		)
          self.assertIsInstance(processor.tokenizer			,     lowerCamelCase		)

          self.assertEqual(processor.image_processor.to_json_string()			,     image_processor_add_kwargs.to_json_string()		)
          self.assertIsInstance(processor.image_processor			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							self.get_image_processor()
          __A	:       Optional[Any]      =							self.get_tokenizer()

          __A	:       List[str]      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)

          __A	:       int      =							self.prepare_image_inputs()

          __A	:       Optional[int]      =							image_processor(lowerCamelCase			,     return_tensors="np"		)
          __A	:       Dict      =							processor(images=lowerCamelCase			,     return_tensors="np"		)

          for key in input_image_proc.keys():
               self.assertAlmostEqual(input_image_proc[key].sum()			,     input_processor[key].sum()			,     delta=1E-2		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							self.get_image_processor()
          __A	:       int      =							self.get_tokenizer()

          __A	:       Optional[int]      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)

          __A	:       Optional[Any]      =							"lower newer"

          __A	:       Union[str, Any]      =							processor(text=lowerCamelCase		)

          __A	:       int      =							tokenizer(lowerCamelCase			,     padding="max_length"			,     max_length=64		)

          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]			,     encoded_processor[key]		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							self.get_image_processor()
          __A	:       Optional[Any]      =							self.get_tokenizer()

          __A	:       int      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)

          __A	:       Tuple      =							"lower newer"
          __A	:       int      =							self.prepare_image_inputs()

          __A	:       str      =							processor(text=lowerCamelCase			,     images=lowerCamelCase		)

          self.assertListEqual(list(inputs.keys()		)			,     ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]		)

          # test if it raises when no input is passed
          with pytest.raises(lowerCamelCase		):
               processor()


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							self.get_image_processor()
          __A	:       Tuple      =							self.get_tokenizer()

          __A	:       Dict      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)

          __A	:       Optional[int]      =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __A	:       Optional[int]      =							processor.batch_decode(lowerCamelCase		)
          __A	:       Optional[int]      =							tokenizer.batch_decode(lowerCamelCase		)

          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							self.get_image_processor()
          __A	:       Union[str, Any]      =							self.get_tokenizer()

          __A	:       Union[str, Any]      =							AlignProcessor(tokenizer=lowerCamelCase			,     image_processor=lowerCamelCase		)

          __A	:       int      =							"lower newer"
          __A	:       str      =							self.prepare_image_inputs()

          __A	:       Tuple      =							processor(text=lowerCamelCase			,     images=lowerCamelCase		)

          self.assertListEqual(list(inputs.keys()		)			,     processor.model_input_names		)

