

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE			,			unittest.TestCase							):
  _a									=  BarthezTokenizer
  _a									=  BarthezTokenizerFast
  _a									=  True
  _a									=  True




  def   A__     (				self					)  -> List[str]:
        '''simple docstring'''






        super().setUp()

        _lowercase	       =BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'					)
        tokenizer.save_pretrained(self.tmpdirname					)
        tokenizer.save_pretrained(self.tmpdirname							,   legacy_format=lowerCAmelCase					)
        _lowercase	       =tokenizer




  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        _lowercase	       ='<pad>'
        _lowercase	       =1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase					)							,   lowerCAmelCase					)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase					)							,   lowerCAmelCase					)




  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =list(self.get_tokenizer().get_vocab().keys()					)

        self.assertEqual(vocab_keys[0]							,   '<s>'					)
        self.assertEqual(vocab_keys[1]							,   '<pad>'					)
        self.assertEqual(vocab_keys[-1]							,   '<mask>'					)
        self.assertEqual(len(lowerCAmelCase					)							,   101_122					)




  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        self.assertEqual(self.get_tokenizer().vocab_size							,   101_122					)




  @require_torch
  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        _lowercase	       =['A long paragraph for summarization.', 'Another paragraph for summarization.']
        _lowercase	       =[0, 57, 3_018, 70_307, 91, 2]

        _lowercase	       =self.tokenizer(
            lowerCAmelCase							,   max_length=len(lowerCAmelCase					)							,   padding=lowerCAmelCase							,   truncation=lowerCAmelCase							,   return_tensors='pt'					)
        self.assertIsInstance(lowerCAmelCase							,   lowerCAmelCase					)

        self.assertEqual((2, 6)							,   batch.input_ids.shape					)
        self.assertEqual((2, 6)							,   batch.attention_mask.shape					)
        _lowercase	       =batch.input_ids.tolist()[0]
        self.assertListEqual(lowerCAmelCase							,   lowerCAmelCase					)




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        if not self.test_rust_tokenizer:
              return

        _lowercase	       =self.get_tokenizer()
        _lowercase	       =self.get_rust_tokenizer()

        _lowercase	       ='I was born in 92000, and this is falsé.'

        _lowercase	       =tokenizer.tokenize(lowerCAmelCase					)
        _lowercase	       =rust_tokenizer.tokenize(lowerCAmelCase					)
        self.assertListEqual(lowerCAmelCase							,   lowerCAmelCase					)

        _lowercase	       =tokenizer.encode(lowerCAmelCase							,   add_special_tokens=lowerCAmelCase					)
        _lowercase	       =rust_tokenizer.encode(lowerCAmelCase							,   add_special_tokens=lowerCAmelCase					)
        self.assertListEqual(lowerCAmelCase							,   lowerCAmelCase					)

        _lowercase	       =self.get_rust_tokenizer()
        _lowercase	       =tokenizer.encode(lowerCAmelCase					)
        _lowercase	       =rust_tokenizer.encode(lowerCAmelCase					)
        self.assertListEqual(lowerCAmelCase							,   lowerCAmelCase					)




  @slow
  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        _lowercase	       ={'input_ids': [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # moussaKam/mbarthez is a french model. So we also use french texts.
        _lowercase	       =[
            'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
            'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
            'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
            'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
            'telles que la traduction et la synthèse de texte.',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=lowerCAmelCase							,   model_name='moussaKam/mbarthez'							,   revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'							,   sequences=lowerCAmelCase							,   )





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def      a      (			A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int     )    ->      np.ndarray:





      """simple docstring"""



      if (ksize % 2) == 0:
            _lowercase	       =ksize + 1
      _lowercase	       =np.zeros((ksize, ksize)      ,   dtype=np.floataa     )

      # each value
      for y in range(A__     ):
            for x in range(A__     ):
                  # distance from center
                  _lowercase	       =x - ksize // 2
                  _lowercase	       =y - ksize // 2

                  # degree to radiant
                  _lowercase	       =theta / 180 * np.pi
                  _lowercase	       =np.cos(_theta     )
                  _lowercase	       =np.sin(_theta     )

                  # get kernel x
                  _lowercase	       =cos_theta * px + sin_theta * py

                  # get kernel y
                  _lowercase	       =-sin_theta * px + cos_theta * py

                  # fill kernel
                  _lowercase	       =np.exp(
                      -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)     ) * np.cos(2 * np.pi * _x / lambd + psi     )

      return gabor


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # read original image
  lowercase_									=   imread('../image_data/lena.jpg')
  # turn image in gray scale value
  lowercase_									=   cvtColor(img, COLOR_BGR2GRAY)

  # Apply multiple Kernel to detect edges
  lowercase_									=   np.zeros(gray.shape[:2])
  for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
    lowercase_									=   gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
    out += filteraD(gray, CV_8UC3, kernel_aa)
  lowercase_									=   out / out.max() * 2_5_5
  lowercase_									=   out.astype(np.uinta)

  imshow('Original', gray)
  imshow('Gabor filter with 20x20 mask and 6 directions', out)

  waitKey(0)


