




'''simple docstring'''







import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 					_a (a__,		unittest.TestCase	):





     '''simple docstring'''



     lowerCAmelCase_  :     List[Any]          =					RobertaTokenizer
     lowerCAmelCase_  :     List[str]          =					RobertaTokenizerFast
     lowerCAmelCase_  :     Union[str, Any]          =					True
     lowerCAmelCase_  :     Optional[Any]          =					{"""cls_token""": """<s>"""}







     def 			snake_case_      (				self							)      ->				Optional[Any]:
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          snake_case			:      Dict        =			[
              """l""",
              """o""",
              """w""",
              """e""",
              """r""",
              """s""",
              """t""",
              """i""",
              """d""",
              """n""",
              """\u0120""",
              """\u0120l""",
              """\u0120n""",
              """\u0120lo""",
              """\u0120low""",
              """er""",
              """\u0120lowest""",
              """\u0120newer""",
              """\u0120wider""",
              """<unk>""",
          ]
          snake_case			:      Optional[int]        =			dict(zip(__a							,range(len(__a							)							)							)							)
          snake_case			:      Dict        =			["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
          snake_case			:      str        =			{"""unk_token""": """<unk>"""}

          snake_case			:      int        =			os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES["""vocab_file"""]							)
          snake_case			:      Dict        =			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 			snake_case_      (				self							,**__a							)      ->				int:
          kwargs.update(self.special_tokens_map							)
          return self.tokenizer_class.from_pretrained(self.tmpdirname							,**__a							)







     def 			snake_case_      (				self							,**__a							)      ->				Optional[Any]:
          kwargs.update(self.special_tokens_map							)
          return RobertaTokenizerFast.from_pretrained(self.tmpdirname							,**__a							)







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      Optional[int]        =			"""lower newer"""
          snake_case			:      List[str]        =			"""lower newer"""
          return input_text, output_text







     def 			snake_case_      (				self							)      ->				int:
          snake_case			:      Optional[Any]        =			self.tokenizer_class(self.vocab_file							,self.merges_file							,**self.special_tokens_map							)
          snake_case			:      List[Any]        =			"""lower newer"""
          snake_case			:      List[str]        =			["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
          snake_case			:      List[str]        =			tokenizer.tokenize(__a							)  # , add_prefix_space=True)
          self.assertListEqual(__a							,__a							)

          snake_case			:      Union[str, Any]        =			tokens + [tokenizer.unk_token]
          snake_case			:      Union[str, Any]        =			[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(__a							)							,__a							)







     def 			snake_case_      (				self							)      ->				Tuple:
          snake_case			:      str        =			self.get_tokenizer()

          self.assertListEqual(tokenizer.encode("""Hello world!"""							,add_special_tokens=__a							)							,[0, 31_414, 232, 328, 2]							)
          self.assertListEqual(
              tokenizer.encode("""Hello world! cécé herlolip 418"""							,add_special_tokens=__a							)							,[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]							,)







     @slow
     def 			snake_case_      (				self							)      ->				List[str]:
          snake_case			:      List[Any]        =			self.tokenizer_class.from_pretrained("""roberta-base"""							)

          snake_case			:      Optional[Any]        =			tokenizer.encode("""sequence builders"""							,add_special_tokens=__a							)
          snake_case			:      int        =			tokenizer.encode("""multi-sequence build"""							,add_special_tokens=__a							)

          snake_case			:      List[str]        =			tokenizer.encode(
              """sequence builders"""							,add_special_tokens=__a							,add_prefix_space=__a							)
          snake_case			:      int        =			tokenizer.encode(
              """sequence builders"""							,"""multi-sequence build"""							,add_special_tokens=__a							,add_prefix_space=__a							)

          snake_case			:      Union[str, Any]        =			tokenizer.build_inputs_with_special_tokens(__a							)
          snake_case			:      List[str]        =			tokenizer.build_inputs_with_special_tokens(__a							,__a							)

          assert encoded_sentence == encoded_text_from_decode
          assert encoded_pair == encoded_pair_from_decode







     def 			snake_case_      (				self							)      ->				List[Any]:
          snake_case			:      Dict        =			self.get_tokenizer()

          snake_case			:      Optional[Any]        =			"""Encode this sequence."""
          snake_case			:      Dict        =			tokenizer.byte_encoder[""" """.encode("""utf-8"""							)[0]]

          # Testing encoder arguments
          snake_case			:      int        =			tokenizer.encode(__a							,add_special_tokens=__a							,add_prefix_space=__a							)
          snake_case			:      List[Any]        =			tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
          self.assertNotEqual(__a							,__a							)

          snake_case			:      Optional[int]        =			tokenizer.encode(__a							,add_special_tokens=__a							,add_prefix_space=__a							)
          snake_case			:      Optional[int]        =			tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
          self.assertEqual(__a							,__a							)

          tokenizer.add_special_tokens({"""bos_token""": """<s>"""}							)
          snake_case			:      Tuple        =			tokenizer.encode(__a							,add_special_tokens=__a							)
          snake_case			:      List[str]        =			tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
          self.assertNotEqual(__a							,__a							)

          # Testing spaces after special tokens
          snake_case			:      Optional[int]        =			"""<mask>"""
          tokenizer.add_special_tokens(
              {"""mask_token""": AddedToken(__a							,lstrip=__a							,rstrip=__a							)}							)  # mask token has a left space
          snake_case			:      Optional[int]        =			tokenizer.convert_tokens_to_ids(__a							)

          snake_case			:      Tuple        =			"""Encode <mask> sequence"""
          snake_case			:      Optional[int]        =			"""Encode <mask>sequence"""

          snake_case			:      Dict        =			tokenizer.encode(__a							)
          snake_case			:      Union[str, Any]        =			encoded.index(__a							)
          snake_case			:      List[Any]        =			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
          self.assertEqual(__a							,__a							)

          snake_case			:      Dict        =			tokenizer.encode(__a							)
          snake_case			:      Any        =			encoded.index(__a							)
          snake_case			:      Tuple        =			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
          self.assertNotEqual(__a							,__a							)







     def 			snake_case_      (				self							)      ->				Optional[Any]:
          pass







     def 			snake_case_      (				self							)      ->				List[str]:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
                    snake_case			:      Optional[int]        =			self.rust_tokenizer_class.from_pretrained(__a							,**__a							)
                    snake_case			:      Tuple        =			self.tokenizer_class.from_pretrained(__a							,**__a							)
                    snake_case			:      int        =			"""A, <mask> AllenNLP sentence."""
                    snake_case			:      Union[str, Any]        =			tokenizer_r.encode_plus(__a							,add_special_tokens=__a							,return_token_type_ids=__a							)
                    snake_case			:      Optional[Any]        =			tokenizer_p.encode_plus(__a							,add_special_tokens=__a							,return_token_type_ids=__a							)

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r["""token_type_ids"""]							)							,sum(tokens_p["""token_type_ids"""]							)							)

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							)							,sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							)							,)

                    snake_case			:      List[str]        =			tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
                    snake_case			:      List[str]        =			tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p["""input_ids"""]							,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]							)
                    self.assertSequenceEqual(tokens_r["""input_ids"""]							,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]							)

                    self.assertSequenceEqual(
                        __a							,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)
                    self.assertSequenceEqual(
                        __a							,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)







     def 			snake_case_      (				self							)      ->				int:
          for trim_offsets, add_prefix_space in itertools.product([True, False]							,repeat=2							):
               snake_case			:      Any        =			self.rust_tokenizer_class.from_pretrained(
                   self.tmpdirname							,use_fast=__a							,add_prefix_space=__a							,trim_offsets=__a							)

               snake_case			:      int        =			json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
               snake_case			:      str        =			json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

               self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]							,__a							)

               self.assertEqual(post_processor_state["""add_prefix_space"""]							,__a							)
               self.assertEqual(post_processor_state["""trim_offsets"""]							,__a							)







     def 			snake_case_      (				self							)      ->				Tuple:
          # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
          # `trim_offsets`
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
                    snake_case			:      Union[str, Any]        =			"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                    snake_case			:      Any        =			F'''{text_of_1_token} {text_of_1_token}'''

                    snake_case			:      List[str]        =			self.rust_tokenizer_class.from_pretrained(
                        __a							,use_fast=__a							,add_prefix_space=__a							,trim_offsets=__a							)
                    snake_case			:      List[Any]        =			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							))							,)

                    snake_case			:      Optional[int]        =			self.rust_tokenizer_class.from_pretrained(
                        __a							,use_fast=__a							,add_prefix_space=__a							,trim_offsets=__a							)
                    snake_case			:      Any        =			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							))							,)

                    snake_case			:      int        =			self.rust_tokenizer_class.from_pretrained(
                        __a							,use_fast=__a							,add_prefix_space=__a							,trim_offsets=__a							)
                    snake_case			:      Any        =			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							), len(__a							) + 1 + len(__a							))							,)

                    snake_case			:      Optional[Any]        =			self.rust_tokenizer_class.from_pretrained(
                        __a							,use_fast=__a							,add_prefix_space=__a							,trim_offsets=__a							)
                    snake_case			:      List[Any]        =			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							), len(__a							) + 1 + len(__a							))							,)

                    snake_case			:      Any        =			F''' {text}'''

                    # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                    # )
                    # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                    # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                    # self.assertEqual(
                    #     encoding.offset_mapping[1],
                    #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                    # )

                    snake_case			:      str        =			self.rust_tokenizer_class.from_pretrained(
                        __a							,use_fast=__a							,add_prefix_space=__a							,trim_offsets=__a							)
                    snake_case			:      int        =			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							))							,)

                    snake_case			:      Optional[int]        =			self.rust_tokenizer_class.from_pretrained(
                        __a							,use_fast=__a							,add_prefix_space=__a							,trim_offsets=__a							)
                    snake_case			:      Tuple        =			tokenizer_r(__a							,return_offsets_mapping=__a							,add_special_tokens=__a							)
                    self.assertEqual(encoding.offset_mapping[0]							,(0, 1 + len(__a							))							)
                    self.assertEqual(
                        encoding.offset_mapping[1]							,(1 + len(__a							), 1 + len(__a							) + 1 + len(__a							))							,)

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









'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase      :   Any      =       imread(R"""digital_image_processing/image_data/lena_small.jpg""")
lowercase      :   List[str]      =       cvtColor(img, COLOR_BGR2GRAY)



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[int]        =			cn.convert_to_negative(__lowercase				)
     # assert negative_img array for at least one True
     assert negative_img.any()



def        lowerCamelCase__				(      ):
     with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
          # Work around assertion for response
          assert str(cc.change_contrast(__lowercase     ,   110				)				).startswith(
              """<PIL.Image.Image image mode=RGB size=100x100 at"""				)



def        lowerCamelCase__				(      ):
     snake_case			:      Union[str, Any]        =			canny.gen_gaussian_kernel(9     ,   sigma=1.4				)
     # Assert ambiguous array
     assert resp.all()



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[Any]        =			imread("""digital_image_processing/image_data/lena_small.jpg"""     ,   0				)
     # assert ambiguous array for all == True
     assert canny_img.all()
     snake_case			:      Any        =			canny.canny(__lowercase				)
     # assert canny array for at least one True
     assert canny_array.any()



def        lowerCamelCase__				(      ):
     assert gg.gaussian_filter(__lowercase     ,   5     ,   sigma=0.9				).all()



def        lowerCamelCase__				(      ):
     # laplace diagonals
     snake_case			:      int        =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
     snake_case			:      Tuple        =			conv.img_convolve(__lowercase     ,   __lowercase				).astype(__lowercase				)
     assert res.any()



def        lowerCamelCase__				(      ):
     assert med.median_filter(__lowercase     ,   3				).any()



def        lowerCamelCase__				(      ):
     snake_case ,		snake_case			:      Dict        =			sob.sobel_filter(__lowercase				)
     assert grad.any() and theta.any()



def        lowerCamelCase__				(      ):
     snake_case			:      int        =			sp.make_sepia(__lowercase     ,   20				)
     assert sepia.all()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"				):
     snake_case			:      int        =			bs.Burkes(imread(__lowercase     ,   1				)     ,   120				)
     burkes.process()
     assert burkes.output_img.any()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"     ,   ):
     snake_case			:      Dict        =			rs.NearestNeighbour(imread(__lowercase     ,   1				)     ,   400     ,   200				)
     nn.process()
     assert nn.output.any()







def        lowerCamelCase__				(      ):
     snake_case			:      int        =			"""digital_image_processing/image_data/lena.jpg"""

     # Reading the image and converting it to grayscale.
     snake_case			:      Tuple        =			imread(__lowercase     ,   0				)

     # Test for get_neighbors_pixel function() return not None
     snake_case			:      Optional[int]        =			0
     snake_case			:      Tuple        =			0
     snake_case			:      Any        =			image[x_coordinate][y_coordinate]

     snake_case			:      Any        =			lbp.get_neighbors_pixel(
         __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				)

     assert neighbors_pixels is not None

     # Test for local_binary_pattern function()
     # Create a numpy array as the same height and width of read image
     snake_case			:      List[Any]        =			np.zeros((image.shape[0], image.shape[1])				)

     # Iterating through the image and calculating the local binary pattern value
     # for each pixel.
     for i in range(0     ,   image.shape[0]				):
          for j in range(0     ,   image.shape[1]				):
               snake_case			:      Optional[Any]        =			lbp.local_binary_value(__lowercase     ,   __lowercase     ,   __lowercase				)

     assert lbp_image.any()



