



import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
lowerCAmelCase					:				int   	='stas/tiny-wmt19-en-ru'






class 	_a			(      snake_case_     , unittest.TestCase  ):
			_UpperCamelCase:   Any	     =		FSMTTokenizer
			_UpperCamelCase:   str	     =		False






			def 							_snake_case   (    self )	->					Any:
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									lowerCAmelCase	:   Tuple					     =					[
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """w</w>""",
									    """r</w>""",
									    """t</w>""",
									    """lo""",
									    """low""",
									    """er</w>""",
									    """low</w>""",
									    """lowest</w>""",
									    """newer</w>""",
									    """wider</w>""",
									    """<unk>""",
									]
									lowerCAmelCase	:   List[str]					     =					dict(zip(lowercase_    ,  range(len(lowercase_ ) ) ) )
									lowerCAmelCase	:   Dict					     =					["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

									lowerCAmelCase	:   int					     =					["""en""", """ru"""]
									lowerCAmelCase	:   List[str]					     =					{
									    """langs""": self.langs,
									    """src_vocab_size""": 10,
									    """tgt_vocab_size""": 20,
									}

									lowerCAmelCase	:   Tuple					     =					os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES["""src_vocab_file"""] )
									lowerCAmelCase	:   Union[str, Any]					     =					os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES["""tgt_vocab_file"""] )
									lowerCAmelCase	:   List[str]					     =					os.path.join(self.tmpdirname    ,  """tokenizer_config.json""" )
									lowerCAmelCase	:   List[str]					     =					os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES["""merges_file"""] )
									with open(self.src_vocab_file    ,  """w""" ) as fp:
															fp.write(json.dumps(lowercase_ ) )
									with open(self.tgt_vocab_file    ,  """w""" ) as fp:
															fp.write(json.dumps(lowercase_ ) )
									with open(self.merges_file    ,  """w""" ) as fp:
															fp.write("""\n""".join(lowercase_ ) )
									with open(lowercase_    ,  """w""" ) as fp:
															fp.write(json.dumps(lowercase_ ) )






			@cached_property
			def 							_snake_case   (    self )	->					Union[str, Any]:
									return FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en""" )






			@cached_property
			def 							_snake_case   (    self )	->					Dict:
									return FSMTTokenizer.from_pretrained("""facebook/wmt19-en-ru""" )






			def 							_snake_case   (    self )	->					str:
									lowerCAmelCase	:   Tuple					     =					FSMTTokenizer.from_pretrained(lowercase_ )
									self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]    ,  ["""en""", """ru"""] )
									self.assertEqual(tokenizer.src_vocab_size    ,  21 )
									self.assertEqual(tokenizer.tgt_vocab_size    ,  21 )






			def 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase	:   List[Any]					     =					FSMTTokenizer(self.langs    ,  self.src_vocab_file    ,  self.tgt_vocab_file    ,  self.merges_file )

									lowerCAmelCase	:   str					     =					"""lower"""
									lowerCAmelCase	:   Any					     =					["""low""", """er</w>"""]
									lowerCAmelCase	:   Any					     =					tokenizer.tokenize(lowercase_ )
									self.assertListEqual(lowercase_    ,  lowercase_ )

									lowerCAmelCase	:   Union[str, Any]					     =					tokens + ["""<unk>"""]
									lowerCAmelCase	:   int					     =					[14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ )    ,  lowercase_ )






			@slow
			def 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase	:   int					     =					self.tokenizer_ru_en

									lowerCAmelCase	:   Tuple					     =					tokenizer.encode("""sequence builders"""    ,  add_special_tokens=lowercase_ )
									lowerCAmelCase	:   List[str]					     =					tokenizer.encode("""multi-sequence build"""    ,  add_special_tokens=lowercase_ )

									lowerCAmelCase	:   str					     =					tokenizer.build_inputs_with_special_tokens(lowercase_ )
									lowerCAmelCase	:   str					     =					tokenizer.build_inputs_with_special_tokens(lowercase_    ,  lowercase_ )

									assert encoded_sentence == text + [2]
									assert encoded_pair == text + [2] + text_a + [2]






			@slow
			def 							_snake_case   (    self )	->					Any:
									lowerCAmelCase	:   Union[str, Any]					     =					self.tokenizer_en_ru
									lowerCAmelCase	:   Dict					     =					self.tokenizer_ru_en

									lowerCAmelCase	:   Union[str, Any]					     =					[
									    [
									        """Here's a little song I wrote. Don't worry, be happy.""",
									        [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 13160, 5, 26, 6445, 7, 2],
									    ],
									    ["""This is it. No more. I'm done!""", [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 427, 2]],
									]

									# if data needs to be recreated or added, run:
									# import torch
									# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
									# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

									for src_text, tgt_input_ids in targets:
															lowerCAmelCase	:   Optional[Any]					     =					tokenizer_enc.encode(lowercase_    ,  return_tensors=lowercase_ )
															self.assertListEqual(lowercase_    ,  lowercase_ )

															# and decode backward, using the reversed languages model
															lowerCAmelCase	:   Union[str, Any]					     =					tokenizer_dec.decode(lowercase_    ,  skip_special_tokens=lowercase_ )
															self.assertEqual(lowercase_    ,  lowercase_ )






			@slow
			def 							_snake_case   (    self )	->					List[str]:
									lowerCAmelCase	:   List[str]					     =					FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""    ,  do_lower_case=lowercase_ )
									lowerCAmelCase	:   Dict					     =					tokenizer.tokenize("""USA is United States of America""" )
									lowerCAmelCase	:   Optional[int]					     =					["""us""", """a</w>""", """is</w>""", """un""", """i""", """ted</w>""", """st""", """ates</w>""", """of</w>""", """am""", """er""", """ica</w>"""]
									self.assertListEqual(lowercase_    ,  lowercase_ )






			@unittest.skip("""FSMTConfig.__init__  requires non-optional args""" )
			def 							_snake_case   (    self )	->					Dict:
									pass






			@unittest.skip("""FSMTConfig.__init__  requires non-optional args""" )
			def 							_snake_case   (    self )	->					Tuple:
									pass






import cva
import numpy as np







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						try:
												return int(image[x_coordinate][y_coordinate] >= center  )
						except (IndexError, TypeError):
												return 0







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   Any					     =					image[x_coordinate][y_coordinate]
						lowerCAmelCase	:   int					     =					[1, 2, 4, 8, 1_6, 3_2, 6_4, 1_2_8]

						# skip get_neighbors_pixel if center is null
						if center is None:
												return 0

						# Starting from the top right, assigning value to pixels clockwise
						lowerCAmelCase	:   Union[str, Any]					     =					[
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate - 1			,y_coordinate + 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,y_coordinate + 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate - 1			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate + 1			,y_coordinate + 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate + 1			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate + 1			,y_coordinate - 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,y_coordinate - 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate - 1			,y_coordinate - 1			,SCREAMING_SNAKE_CASE__  ),
						]

						# Converting the binary value to decimal.
						return sum(
						    binary_value * power for binary_value, power in zip(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )  )


if __name__ == "__main__":
		# Reading the image and converting it to grayscale.
		lowerCAmelCase					:				Dict   	=cva.imread(
		    'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
		)

		# Create a numpy array as the same height and width of read image
		lowerCAmelCase					:				Dict   	=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]):
						lowerCAmelCase					:				Optional[int]   	=local_binary_value(image, i, j)

		cva.imshow('local binary pattern', lbp_image)
		cva.waitKey(0)
		cva.destroyAllWindows()

