


'''simple docstring'''



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 			a       (	_a			,						unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				List[str]							     =     CLIPTokenizer
     SCREAMING_SNAKE_CASE :				Optional[int]							     =     CLIPTokenizerFast
     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     True
     SCREAMING_SNAKE_CASE :				int							     =     {}
     SCREAMING_SNAKE_CASE :				List[Any]							     =     False

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Tuple:
          super().setUp()

          # fmt: off
          __UpperCAmelCase			:				str													=    ['''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
          __UpperCAmelCase			:				Any													=    dict(zip(snake_case							,				range(len(snake_case    )    )    )    )
          __UpperCAmelCase			:				List[str]													=    ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
          __UpperCAmelCase			:				Dict													=    {'''unk_token''': '''<unk>'''}

          __UpperCAmelCase			:				str													=    os.path.join(self.tmpdirname							,				VOCAB_FILES_NAMES['''vocab_file''']    )
          __UpperCAmelCase			:				Any													=    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(snake_case    ) + '''\n'''    )
          with open(self.merges_file							,				'''w'''							,				encoding='''utf-8'''    ) as fp:
               fp.write('''\n'''.join(snake_case    )    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				**snake_case  :	Union[str, Any]    )		->							Optional[Any]:
          kwargs.update(self.special_tokens_map    )
          return CLIPTokenizer.from_pretrained(self.tmpdirname							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	List[Any]							,				**snake_case  :	int    )		->							Optional[Any]:
          kwargs.update(self.special_tokens_map    )
          return CLIPTokenizerFast.from_pretrained(self.tmpdirname							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Any    )		->							Any:
          __UpperCAmelCase			:				int													=    '''lower newer'''
          __UpperCAmelCase			:				List[Any]													=    '''lower newer'''
          return input_text, output_text

     def 				lowerCamelCase__				(       self  :	Dict    )		->							Optional[int]:
          __UpperCAmelCase			:				str													=    CLIPTokenizer(self.vocab_file							,				self.merges_file							,				**self.special_tokens_map    )
          __UpperCAmelCase			:				int													=    '''lower newer'''
          __UpperCAmelCase			:				Optional[int]													=    ['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
          __UpperCAmelCase			:				Any													=    tokenizer.tokenize(snake_case    )
          self.assertListEqual(snake_case							,				snake_case    )

          __UpperCAmelCase			:				Union[str, Any]													=    tokens + [tokenizer.unk_token]
          __UpperCAmelCase			:				Any													=    [10, 2, 16, 9, 3, 2, 16, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case    )							,				snake_case    )

     @require_ftfy
     def 				lowerCamelCase__				(       self  :	str    )		->							List[Any]:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
                    __UpperCAmelCase			:				Dict													=    self.tokenizer_class.from_pretrained(snake_case							,				**snake_case    )
                    __UpperCAmelCase			:				List[Any]													=    self.rust_tokenizer_class.from_pretrained(snake_case							,				**snake_case    )

                    __UpperCAmelCase			:				Optional[int]													=    '''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
                    __UpperCAmelCase			:				int													=    tokenizer_s.tokenize(snake_case    )
                    __UpperCAmelCase			:				Optional[int]													=    tokenizer_r.tokenize(snake_case    )

                    self.assertListEqual(snake_case							,				snake_case    )

                    # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                    # with Tilde) encoded in 2 different ways
                    __UpperCAmelCase			:				Any													=    '''xa\u0303y''' + ''' ''' + '''x\xe3y'''
                    __UpperCAmelCase			:				Optional[Any]													=    tokenizer_s.tokenize(snake_case    )
                    __UpperCAmelCase			:				Optional[int]													=    tokenizer_r.tokenize(snake_case    )

                    self.assertListEqual(snake_case							,				snake_case    )

                    # Test that the tokenization is identical on unicode of space type
                    __UpperCAmelCase			:				List[Any]													=    [
                        '''\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:
                         __UpperCAmelCase			:				Dict													=    tokenizer_s.tokenize(snake_case    )
                         __UpperCAmelCase			:				List[str]													=    tokenizer_r.tokenize(snake_case    )

                         self.assertListEqual(snake_case							,				snake_case    )

                    # Test that the tokenization is identical on unicode of line break type
                    __UpperCAmelCase			:				Optional[int]													=    [
                        '''\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:
                         __UpperCAmelCase			:				Union[str, Any]													=    tokenizer_s.tokenize(snake_case    )
                         __UpperCAmelCase			:				Optional[Any]													=    tokenizer_r.tokenize(snake_case    )

                         self.assertListEqual(snake_case							,				snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Any:
          # 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})'    ):
                    __UpperCAmelCase			:				Optional[int]													=    '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                    __UpperCAmelCase			:				Optional[int]													=    f'{text_of_1_token} {text_of_1_token}'

                    __UpperCAmelCase			:				List[str]													=    self.rust_tokenizer_class.from_pretrained(
                        snake_case							,				use_fast=snake_case							,				)
                    __UpperCAmelCase			:				List[str]													=    tokenizer_r(snake_case							,				return_offsets_mapping=snake_case							,				add_special_tokens=snake_case    )
                    self.assertEqual(encoding.offset_mapping[0]							,				(0, len(snake_case    ))    )
                    self.assertEqual(
                        encoding.offset_mapping[1]							,				(len(snake_case    ) + 1, len(snake_case    ) + 1 + len(snake_case    ))							,				)

                    __UpperCAmelCase			:				int													=    f' {text}'

                    __UpperCAmelCase			:				Tuple													=    self.rust_tokenizer_class.from_pretrained(
                        snake_case							,				use_fast=snake_case							,				)
                    __UpperCAmelCase			:				Union[str, Any]													=    tokenizer_r(snake_case							,				return_offsets_mapping=snake_case							,				add_special_tokens=snake_case    )
                    self.assertEqual(encoding.offset_mapping[0]							,				(1, 1 + len(snake_case    ))    )
                    self.assertEqual(
                        encoding.offset_mapping[1]							,				(1 + len(snake_case    ) + 1, 1 + len(snake_case    ) + 1 + len(snake_case    ))							,				)

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							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(snake_case    ) 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 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							str:
          super().test_tokenization_python_rust_equals()






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



'''simple docstring'''



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			(		_lowercase      :			Optional[Any]    ,      _lowercase      :			List[Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    k_size // 2
     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __UpperCAmelCase			:				Optional[int]													=    1 / (2 * pi * sigma) * exp(-(square(_lowercase						) + square(_lowercase						)) / (2 * square(_lowercase						))						)
     return g




def 			_a			(		_lowercase      :			str    ,      _lowercase      :			List[str]    ,      _lowercase      :			Optional[Any]						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    image.shape[0], image.shape[1]
     # dst image height and width
     __UpperCAmelCase			:				Any													=    height - k_size + 1
     __UpperCAmelCase			:				str													=    width - k_size + 1

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

     #  turn the kernel into shape(k*k, 1)
     __UpperCAmelCase			:				str													=    gen_gaussian_kernel(_lowercase    ,      _lowercase						)
     __UpperCAmelCase			:				Union[str, Any]													=    ravel(_lowercase						)

     # reshape and get the dst image
     __UpperCAmelCase			:				Optional[int]													=    dot(_lowercase    ,      _lowercase						).reshape(_lowercase    ,      _lowercase						).astype(_lowercase						)

     return dst


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

      # get values with two different mask size
      __UpperCAmelCase    :Tuple				       =     gaussian_filter(gray, 3, sigma=1)
      __UpperCAmelCase    :Optional[Any]				       =     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()