




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
       from transformers.models.mbart.modeling_mbart import shift_tokens_right

SCREAMING_SNAKE_CASE__       :      str  =   250004
SCREAMING_SNAKE_CASE__       :      Dict  =   250020




@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__	(lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[int]	     =      MBartTokenizer
    lowerCamelCase_      :	Dict	     =      MBartTokenizerFast
    lowerCamelCase_      :	Optional[Any]	     =      True
    lowerCamelCase_      :	str	     =      True







    def     _lowercase     (    self     )     ->      int:
           super().setUp()

           # We have a SentencePiece fixture for testing
           lowerCamelCase   :      Tuple			 =					MBartTokenizer(UpperCamelCase__     ,  keep_accents=UpperCamelCase__     )
           tokenizer.save_pretrained(self.tmpdirname     )







    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      Union[str, Any]			 =					MBartTokenizer(UpperCamelCase__     ,  keep_accents=UpperCamelCase__     )

           lowerCamelCase   :      Union[str, Any]			 =					tokenizer.tokenize("This is a test"     )
           self.assertListEqual(UpperCamelCase__     ,  ["▁This", "▁is", "▁a", "▁t", "est"]     )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(UpperCamelCase__     )     ,  [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]     ,  )

           lowerCamelCase   :      Tuple			 =					tokenizer.tokenize("I was born in 92000, and this is falsé."     )
           self.assertListEqual(
               UpperCamelCase__     ,  [
                   SPIECE_UNDERLINE + "I",
                   SPIECE_UNDERLINE + "was",
                   SPIECE_UNDERLINE + "b",
                   "or",
                   "n",
                   SPIECE_UNDERLINE + "in",
                   SPIECE_UNDERLINE + "",
                   "9",
                   "2",
                   "0",
                   "0",
                   "0",
                   ",",
                   SPIECE_UNDERLINE + "and",
                   SPIECE_UNDERLINE + "this",
                   SPIECE_UNDERLINE + "is",
                   SPIECE_UNDERLINE + "f",
                   "al",
                   "s",
                   "é",
                   ".",
               ]     ,  )
           lowerCamelCase   :      Dict			 =					tokenizer.convert_tokens_to_ids(UpperCamelCase__     )
           self.assertListEqual(
               UpperCamelCase__     ,  [
                   value + tokenizer.fairseq_offset
                   for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                   #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
               ]     ,  )

           lowerCamelCase   :      List[str]			 =					tokenizer.convert_ids_to_tokens(UpperCamelCase__     )
           self.assertListEqual(
               UpperCamelCase__     ,  [
                   SPIECE_UNDERLINE + "I",
                   SPIECE_UNDERLINE + "was",
                   SPIECE_UNDERLINE + "b",
                   "or",
                   "n",
                   SPIECE_UNDERLINE + "in",
                   SPIECE_UNDERLINE + "",
                   "<unk>",
                   "2",
                   "0",
                   "0",
                   "0",
                   ",",
                   SPIECE_UNDERLINE + "and",
                   SPIECE_UNDERLINE + "this",
                   SPIECE_UNDERLINE + "is",
                   SPIECE_UNDERLINE + "f",
                   "al",
                   "s",
                   "<unk>",
                   ".",
               ]     ,  )







    def     _lowercase     (    self     )     ->      Union[str, Any]:
           if not self.test_slow_tokenizer:
                  # as we don't have a slow version, we can't compare the outputs between slow and fast versions
                  return

           lowerCamelCase   :      List[str]			 =					(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                         lowerCamelCase   :      Dict			 =					self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,  **UpperCamelCase__     )
                         lowerCamelCase   :      int			 =					self.tokenizer_class.from_pretrained(UpperCamelCase__     ,  **UpperCamelCase__     )

                         lowerCamelCase   :      str			 =					tempfile.mkdtemp()

                         lowerCamelCase   :      Dict			 =					tokenizer_r.save_pretrained(UpperCamelCase__     )
                         lowerCamelCase   :      Optional[int]			 =					tokenizer_p.save_pretrained(UpperCamelCase__     )

                         # Checks it save with the same files + the tokenizer.json file for the fast one
                         self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )
                         lowerCamelCase   :      Optional[int]			 =					tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f     )
                         self.assertSequenceEqual(UpperCamelCase__     ,  UpperCamelCase__     )

                         # Checks everything loads correctly in the same way
                         lowerCamelCase   :      Optional[Any]			 =					tokenizer_r.from_pretrained(UpperCamelCase__     )
                         lowerCamelCase   :      Union[str, Any]			 =					tokenizer_p.from_pretrained(UpperCamelCase__     )

                         # Check special tokens are set accordingly on Rust and Python
                         for key in tokenizer_pp.special_tokens_map:
                                self.assertTrue(hasattr(UpperCamelCase__     ,  UpperCamelCase__     )     )
                                # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                                # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                         shutil.rmtree(UpperCamelCase__     )

                         # Save tokenizer rust, legacy_format=True
                         lowerCamelCase   :      Union[str, Any]			 =					tempfile.mkdtemp()

                         lowerCamelCase   :      Tuple			 =					tokenizer_r.save_pretrained(UpperCamelCase__     ,  legacy_format=UpperCamelCase__     )
                         lowerCamelCase   :      Dict			 =					tokenizer_p.save_pretrained(UpperCamelCase__     )

                         # Checks it save with the same files
                         self.assertSequenceEqual(UpperCamelCase__     ,  UpperCamelCase__     )

                         # Checks everything loads correctly in the same way
                         lowerCamelCase   :      Dict			 =					tokenizer_r.from_pretrained(UpperCamelCase__     )
                         lowerCamelCase   :      Dict			 =					tokenizer_p.from_pretrained(UpperCamelCase__     )

                         # Check special tokens are set accordingly on Rust and Python
                         for key in tokenizer_pp.special_tokens_map:
                                self.assertTrue(hasattr(UpperCamelCase__     ,  UpperCamelCase__     )     )

                         shutil.rmtree(UpperCamelCase__     )

                         # Save tokenizer rust, legacy_format=False
                         lowerCamelCase   :      List[Any]			 =					tempfile.mkdtemp()

                         lowerCamelCase   :      str			 =					tokenizer_r.save_pretrained(UpperCamelCase__     ,  legacy_format=UpperCamelCase__     )
                         lowerCamelCase   :      str			 =					tokenizer_p.save_pretrained(UpperCamelCase__     )

                         # Checks it saved the tokenizer.json file
                         self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )

                         # Checks everything loads correctly in the same way
                         lowerCamelCase   :      List[str]			 =					tokenizer_r.from_pretrained(UpperCamelCase__     )
                         lowerCamelCase   :      List[str]			 =					tokenizer_p.from_pretrained(UpperCamelCase__     )

                         # Check special tokens are set accordingly on Rust and Python
                         for key in tokenizer_pp.special_tokens_map:
                                self.assertTrue(hasattr(UpperCamelCase__     ,  UpperCamelCase__     )     )

                         shutil.rmtree(UpperCamelCase__     )




@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	str	     =      """facebook/mbart-large-en-ro"""
    lowerCamelCase_      :	Tuple	     =      [
        """ UN Chief Says There Is No Military Solution in Syria""",
        """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
    ]
    lowerCamelCase_      :	List[str]	     =      [
        """Şeful ONU declară că nu există o soluţie militară în Siria""",
        """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
        """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
        """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
    ]
    lowerCamelCase_      :	str	     =      [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]







    @classmethod
    def     _lowercase     (    cls     )     ->      List[Any]:
           lowerCamelCase   :      MBartTokenizer			 =					MBartTokenizer.from_pretrained(
               cls.checkpoint_name     ,  src_lang="en_XX"     ,  tgt_lang="ro_RO"     )
           lowerCamelCase   :      Dict			 =					1
           return cls







    def     _lowercase     (    self     )     ->      Optional[Any]:
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]     ,  25_0001     )
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]     ,  25_0004     )
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]     ,  25_0020     )







    def     _lowercase     (    self     )     ->      List[str]:
           lowerCamelCase   :      str			 =					self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
           self.assertListEqual(self.expected_src_tokens     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Tuple:
           self.assertIn(UpperCamelCase__     ,  self.tokenizer.all_special_ids     )
           lowerCamelCase   :      List[Any]			 =					[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
           lowerCamelCase   :      Optional[Any]			 =					self.tokenizer.decode(UpperCamelCase__     ,  skip_special_tokens=UpperCamelCase__     )
           lowerCamelCase   :      Tuple			 =					self.tokenizer.decode(generated_ids[1:]     ,  skip_special_tokens=UpperCamelCase__     )
           self.assertEqual(UpperCamelCase__     ,  UpperCamelCase__     )
           self.assertNotIn(self.tokenizer.eos_token     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      Any			 =					["this is gunna be a long sentence " * 20]
           assert isinstance(src_text[0]     ,  UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					10
           lowerCamelCase   :      List[str]			 =					self.tokenizer(UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  truncation=UpperCamelCase__     ).input_ids[0]
           self.assertEqual(ids[-2]     ,  2     )
           self.assertEqual(ids[-1]     ,  UpperCamelCase__     )
           self.assertEqual(len(UpperCamelCase__     )     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      int:
           self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]     )     ,  [25_0026, 25_0001]     )







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      Tuple			 =					tempfile.mkdtemp()
           lowerCamelCase   :      Union[str, Any]			 =					self.tokenizer.fairseq_tokens_to_ids
           self.tokenizer.save_pretrained(UpperCamelCase__     )
           lowerCamelCase   :      int			 =					MBartTokenizer.from_pretrained(UpperCamelCase__     )
           self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,  UpperCamelCase__     )







    @require_torch
    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      Dict			 =					self.tokenizer(self.src_text     ,  text_target=self.tgt_text     ,  padding=UpperCamelCase__     ,  return_tensors="pt"     )
           lowerCamelCase   :      str			 =					shift_tokens_right(batch["labels"]     ,  self.tokenizer.pad_token_id     )

           # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
           assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
           assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
           assert batch.decoder_input_ids[1][-1] == 2
           assert batch.labels[1][-2:].tolist() == [2, RO_CODE]







    @require_torch
    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      Optional[Any]			 =					self.tokenizer(
               self.src_text     ,  text_target=self.tgt_text     ,  padding=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  max_length=len(self.expected_src_tokens     )     ,  return_tensors="pt"     ,  )

           lowerCamelCase   :      Optional[int]			 =					shift_tokens_right(batch["labels"]     ,  self.tokenizer.pad_token_id     )

           self.assertIsInstance(UpperCamelCase__     ,  UpperCamelCase__     )

           self.assertEqual((2, 14)     ,  batch.input_ids.shape     )
           self.assertEqual((2, 14)     ,  batch.attention_mask.shape     )
           lowerCamelCase   :      str			 =					batch.input_ids.tolist()[0]
           self.assertListEqual(self.expected_src_tokens     ,  UpperCamelCase__     )
           self.assertEqual(2     ,  batch.decoder_input_ids[0, -1]     )  # EOS
           # Test that special tokens are reset
           self.assertEqual(self.tokenizer.prefix_tokens     ,  []     )
           self.assertEqual(self.tokenizer.suffix_tokens     ,  [self.tokenizer.eos_token_id, EN_CODE]     )







    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      int			 =					self.tokenizer(self.src_text     ,  padding=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  max_length=3     ,  return_tensors="pt"     )
           lowerCamelCase   :      List[str]			 =					self.tokenizer(
               text_target=self.tgt_text     ,  padding=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  max_length=10     ,  return_tensors="pt"     )
           lowerCamelCase   :      Tuple			 =					targets["input_ids"]
           lowerCamelCase   :      List[Any]			 =					shift_tokens_right(UpperCamelCase__     ,  self.tokenizer.pad_token_id     )

           self.assertEqual(batch.input_ids.shape[1]     ,  3     )
           self.assertEqual(batch.decoder_input_ids.shape[1]     ,  10     )







    @require_torch
    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      Union[str, Any]			 =					self.tokenizer._build_translation_inputs(
               "A test"     ,  return_tensors="pt"     ,  src_lang="en_XX"     ,  tgt_lang="ar_AR"     )

           self.assertEqual(
               nested_simplify(UpperCamelCase__     )     ,  {
                   # A, test, EOS, en_XX
                   "input_ids": [[62, 3034, 2, 25_0004]],
                   "attention_mask": [[1, 1, 1, 1]],
                   # ar_AR
                   "forced_bos_token_id": 25_0001,
               }     ,  )







from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     k_size // 2
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      SCREAMING_SNAKE_CASE_      =     1 / (2 * pi * sigma) * exp(-(square(__UpperCamelCase ) + square(__UpperCamelCase )) / (2 * square(__UpperCamelCase )) )
      return g


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     image.shape[0], image.shape[1]
      # dst image height and width
      SCREAMING_SNAKE_CASE_      =     height - k_size + 1
      SCREAMING_SNAKE_CASE_      =     width - k_size + 1

      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      SCREAMING_SNAKE_CASE_      =     zeros((dst_height * dst_width, k_size * k_size) )
      SCREAMING_SNAKE_CASE_      =     0
      for i, j in product(range(__UpperCamelCase )   ,					range(__UpperCamelCase ) ):
            SCREAMING_SNAKE_CASE_      =     ravel(image[i : i + k_size, j : j + k_size] )
            SCREAMING_SNAKE_CASE_      =     window
            row += 1

      #  turn the kernel into shape(k*k, 1)
      SCREAMING_SNAKE_CASE_      =     gen_gaussian_kernel(__UpperCamelCase   ,					__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     ravel(__UpperCamelCase )

      # reshape and get the dst image
      SCREAMING_SNAKE_CASE_      =     dot(__UpperCamelCase   ,					__UpperCamelCase ).reshape(__UpperCamelCase   ,					__UpperCamelCase ).astype(__UpperCamelCase )

      return dst


if __name__ == "__main__":
      # read original image
      A   :     Tuple       =				imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      A   :     Optional[int]       =				cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      A   :     Tuple       =				gaussian_filter(gray, 3, sigma=1)
      A   :     Optional[int]       =				gaussian_filter(gray, 5, sigma=0.8)

      # show result images
      imshow("gaussian filter with 3x3 mask", gaussianaxa)
      imshow("gaussian filter with 5x5 mask", gaussianaxa)
      waitKey()


