






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							(	UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = CLIPTokenizer
  A_						       = CLIPTokenizerFast
  A_						       = True
  A_						       = {}
  A_						       = False




  def  _UpperCAmelCase  (  self			)      ->			str:
     super().setUp()

     # fmt: off
     UpperCamelCase_						=  ['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_						=  dict(zip(a_		,		range(len(a_			)			)			)			)
     UpperCamelCase_						=  ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
     UpperCamelCase_						=  {'unk_token': '<unk>'}

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




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			str:
     kwargs.update(self.special_tokens_map			)
     return CLIPTokenizer.from_pretrained(self.tmpdirname		,		**a_			)




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			List[str]:
     kwargs.update(self.special_tokens_map			)
     return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,		**a_			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[Any]:
     UpperCamelCase_						=  'lower newer'
     UpperCamelCase_						=  'lower newer'
     return input_text, output_text




  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  CLIPTokenizer(self.vocab_file		,		self.merges_file		,		**self.special_tokens_map			)
     UpperCamelCase_						=  'lower newer'
     UpperCamelCase_						=  ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
     UpperCamelCase_						=  tokenizer.tokenize(a_			)
     self.assertListEqual(a_		,		a_			)

     UpperCamelCase_						=  tokens + [tokenizer.unk_token]
     UpperCamelCase_						=  [10, 2, 16, 9, 3, 2, 16, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(a_			)		,		a_			)




  @require_ftfy
  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
           UpperCamelCase_						=  self.tokenizer_class.from_pretrained(a_		,		**a_			)
           UpperCamelCase_						=  self.rust_tokenizer_class.from_pretrained(a_		,		**a_			)

           UpperCamelCase_						=  'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
           UpperCamelCase_						=  tokenizer_s.tokenize(a_			)
           UpperCamelCase_						=  tokenizer_r.tokenize(a_			)

           self.assertListEqual(a_		,		a_			)

           # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
           # with Tilde) encoded in 2 different ways
           UpperCamelCase_						=  'xa\u0303y' + ' ' + 'x\xe3y'
           UpperCamelCase_						=  tokenizer_s.tokenize(a_			)
           UpperCamelCase_						=  tokenizer_r.tokenize(a_			)

           self.assertListEqual(a_		,		a_			)

           # Test that the tokenization is identical on unicode of space type
           UpperCamelCase_						=  [
               '\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_						=  tokenizer_s.tokenize(a_			)
              UpperCamelCase_						=  tokenizer_r.tokenize(a_			)

              self.assertListEqual(a_		,		a_			)

           # Test that the tokenization is identical on unicode of line break type
           UpperCamelCase_						=  [
               '\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_						=  tokenizer_s.tokenize(a_			)
              UpperCamelCase_						=  tokenizer_r.tokenize(a_			)

              self.assertListEqual(a_		,		a_			)




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

           UpperCamelCase_						=  self.rust_tokenizer_class.from_pretrained(
               a_		,		use_fast=a_		,		)
           UpperCamelCase_						=  tokenizer_r(a_		,		return_offsets_mapping=a_		,		add_special_tokens=a_			)
           self.assertEqual(encoding.offset_mapping[0]		,		(0, len(a_			))			)
           self.assertEqual(
               encoding.offset_mapping[1]		,		(len(a_			) + 1, len(a_			) + 1 + len(a_			))		,		)

           UpperCamelCase_						=  f""" {text}"""

           UpperCamelCase_						=  self.rust_tokenizer_class.from_pretrained(
               a_		,		use_fast=a_		,		)
           UpperCamelCase_						=  tokenizer_r(a_		,		return_offsets_mapping=a_		,		add_special_tokens=a_			)
           self.assertEqual(encoding.offset_mapping[0]		,		(1, 1 + len(a_			))			)
           self.assertEqual(
               encoding.offset_mapping[1]		,		(1 + len(a_			) + 1, 1 + len(a_			) + 1 + len(a_			))		,		)




  def  _UpperCAmelCase  (  self			)      ->			int:
     # 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(a_			) 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  _UpperCAmelCase  (  self			)      ->			int:
     super().test_tokenization_python_rust_equals()




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     pass










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     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  k_size // 2
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				lowerCAmelCase__		  =  1 / (2 * pi * sigma) * exp(-(square(snake_case__					) + square(snake_case__					)) / (2 * square(snake_case__					))					)
				return g







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__  ,   lowerCAmelCase__		  =  image.shape[0], image.shape[1]
				# dst image height and width
				lowerCAmelCase__		  =  height - k_size + 1
				lowerCAmelCase__		  =  width - k_size + 1

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

				#  turn the kernel into shape(k*k, 1)
				lowerCAmelCase__		  =  gen_gaussian_kernel(snake_case__    ,			snake_case__					)
				lowerCAmelCase__		  =  ravel(snake_case__					)

				# reshape and get the dst image
				lowerCAmelCase__		  =  dot(snake_case__    ,			snake_case__					).reshape(snake_case__    ,			snake_case__					).astype(snake_case__					)

				return dst


if __name__ == "__main__":
	# read original image
	_lowerCAmelCase	:   List[str]    =   imread(R"../image_data/lena.jpg")
	# turn image in gray scale value
	_lowerCAmelCase	:   Optional[Any]    =   cvtColor(img, COLOR_BGR2GRAY)

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