





"""simple docstring"""





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like




def 		lowerCAmelCase_     (  lowercase_       :							str			,				lowercase_       :							Tuple=3		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	int(mask / 2		)
 # copy image size
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	zeros_like(lowercase_		)
 for i in range(lowercase_			,				gray_img.shape[0] - bd		):
  for j in range(lowercase_			,				gray_img.shape[1] - bd		):
   # get mask according with mask
   __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]		)
   # calculate mask median
   __SCREAMING_SNAKE_CASE			:      str 							=	sort(lowercase_		)[inta(divide((multiply(lowercase_			,				lowercase_		))			,				2		) + 1		)]
   __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	median
 return median_img


if __name__ == "__main__":
    # read original image
    _lowerCamelCase  					=     imread('''../image_data/lena.jpg''')
    # turn image in gray scale value
    _lowerCamelCase  					=     cvtColor(img, COLOR_BGR2GRAY)

    # get values with two different mask size
    _lowerCamelCase  					=     median_filter(gray, 3)
    _lowerCamelCase  					=     median_filter(gray, 5)

    # show result images
    imshow('''median filter with 3x3 mask''', medianaxa)
    imshow('''median filter with 5x5 mask''', medianaxa)
    waitKey(0)








"""simple docstring"""





import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class       snake_case		(       __UpperCAmelCase			,					unittest.TestCase       ):
      lowerCamelCase__      =			PreTrainedTokenizerFast
      lowerCamelCase__      =			False
      lowerCamelCase__      =			True
      lowerCamelCase__      =			'''tokenizer_file'''

      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	False  # because we don't have pretrained_vocab_files_map
       super().setUp()
       __SCREAMING_SNAKE_CASE			:      Dict 							=	True

       __SCREAMING_SNAKE_CASE			:      int 							=	['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	'''SaulLu/dummy-tokenizer-bytelevel-bpe'''

       # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

       __SCREAMING_SNAKE_CASE			:      str 							=	PreTrainedTokenizerFast.from_pretrained(model_paths[0]						)
       tokenizer.save_pretrained(self.tmpdirname						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						):
       # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
       # model
       pass

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						):
       # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
       # model
       pass

      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						):
       # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
       # model
       pass

      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						):
       # PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
       pass

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	self.tmpdirname
       # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
         try:
          __SCREAMING_SNAKE_CASE			:      int 							=	tempfile.mkdtemp()
          __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.rust_tokenizer_class.from_pretrained(_lowerCamelCase						,							**_lowerCamelCase						)

          tokenizer.save_pretrained(self.tmpdirname						)
          super().test_training_new_tokenizer()
         finally:
          # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
          # is restored
          shutil.rmtree(self.tmpdirname						)
          __SCREAMING_SNAKE_CASE			:      Tuple 							=	tmpdirname_orig

      def 						SCREAMING_SNAKE_CASE_      (      self       :str						):
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.tmpdirname
       # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
         try:
          __SCREAMING_SNAKE_CASE			:      Dict 							=	tempfile.mkdtemp()
          __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.rust_tokenizer_class.from_pretrained(_lowerCamelCase						,							**_lowerCamelCase						)

          tokenizer.save_pretrained(self.tmpdirname						)
          super().test_training_new_tokenizer_with_special_tokens_change()
         finally:
          # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
          # is restored
          shutil.rmtree(self.tmpdirname						)
          __SCREAMING_SNAKE_CASE			:      List[Any] 							=	tmpdirname_orig



      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						):
       __SCREAMING_SNAKE_CASE			:      str 							=	self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name						)

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	('''a''' for _ in range(1_0_0_0						))
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	tokenizer.train_new_from_iterator(text_iterator=_lowerCamelCase						,							length=1_0_0_0						,							vocab_size=5_0						)

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	new_tokenizer.encode('''a🤗'''						)
       self.assertEqual(_lowerCamelCase						,							[6_4, 1_7_2, 2_5_3, 9_7, 2_4_5]						)







@require_tokenizers
class       snake_case		(       unittest.TestCase       ):

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						):
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	AutoTokenizer.from_pretrained('''bert-base-cased'''						)
       __SCREAMING_SNAKE_CASE			:      str 							=	json.loads(tokenizer._tokenizer.to_str()						)
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	len(_lowerCamelCase						)

       with tempfile.TemporaryDirectory() as tmp_dir:
        # Hack to save this in the tokenizer_config.json
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	['''tokenizer.4.0.0.json''']
        tokenizer.save_pretrained(_lowerCamelCase						)
        json.dump(_lowerCamelCase						,							open(os.path.join(_lowerCamelCase						,							'''tokenizer.4.0.0.json'''						)						,							'''w'''						)						)

        # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
        __SCREAMING_SNAKE_CASE			:      Dict 							=	AutoTokenizer.from_pretrained(_lowerCamelCase						)
        self.assertEqual(len(_lowerCamelCase						)						,							len(_lowerCamelCase						) + 1						)
        __SCREAMING_SNAKE_CASE			:      Dict 							=	json.loads(new_tokenizer._tokenizer.to_str()						)
        self.assertIn('''huggingface'''						,							json_tokenizer['''model''']['''vocab''']						)

        # Will need to be adjusted if we reach v42 and this test is still here.
        # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
        shutil.move(os.path.join(_lowerCamelCase						,							'''tokenizer.4.0.0.json'''						)						,							os.path.join(_lowerCamelCase						,							'''tokenizer.42.0.0.json'''						)						)
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	['''tokenizer.42.0.0.json''']
        tokenizer.save_pretrained(_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	AutoTokenizer.from_pretrained(_lowerCamelCase						)
        self.assertEqual(len(_lowerCamelCase						)						,							len(_lowerCamelCase						)						)
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	json.loads(new_tokenizer._tokenizer.to_str()						)
        self.assertNotIn('''huggingface'''						,							json_tokenizer['''model''']['''vocab''']						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						):
       # This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	'''hf-internal-testing/test-two-tokenizers'''

       # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
       __SCREAMING_SNAKE_CASE			:      int 							=	AutoTokenizer.from_pretrained(_lowerCamelCase						)
       self.assertEqual(len(_lowerCamelCase						)						,							2_8_9_9_7						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	json.loads(tokenizer._tokenizer.to_str()						)
       self.assertIn('''huggingface'''						,							json_tokenizer['''model''']['''vocab''']						)

       # Testing an older version by monkey-patching the version in the module it's used.
       import transformers as old_transformers

       __SCREAMING_SNAKE_CASE			:      Dict 							=	'''3.0.0'''
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	old_transformers.models.auto.AutoTokenizer.from_pretrained(_lowerCamelCase						)
       self.assertEqual(len(_lowerCamelCase						)						,							2_8_9_9_6						)
       __SCREAMING_SNAKE_CASE			:      Any 							=	json.loads(old_tokenizer._tokenizer.to_str()						)
       self.assertNotIn('''huggingface'''						,							json_tokenizer['''model''']['''vocab''']						)







@require_tokenizers
class       snake_case		(       unittest.TestCase       ):

      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						):
       # See https://github.com/huggingface/transformers/pull/12550
       # and https://github.com/huggingface/tokenizers/issues/537
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel'''						)
       __SCREAMING_SNAKE_CASE			:      str 							=	'''The Matrix is a 1999 science fiction action film.'''

       with concurrent.futures.ThreadPoolExecutor() as executor:
        __SCREAMING_SNAKE_CASE			:      Dict 							=	[executor.submit(self.fetch						,							_lowerCamelCase						,							_lowerCamelCase						) for i in range(1_0						)]
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	[future.result() for future in futures]
        self.assertEqual(_lowerCamelCase						,							[[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0						)]						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						,							_lowerCamelCase       :int						,							_lowerCamelCase       :Optional[int]						):
       return tokenizer.encode(_lowerCamelCase						,							truncation='''longest_first'''						,							padding='''longest'''						)

