



"""simple docstring"""

import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__			   =  get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
	from transformers.models.plbart.modeling_plbart import shift_tokens_right

lowercase__			   =  50003
lowercase__			   =  50002




@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__					(		UpperCamelCase_     ,					unittest.TestCase	):
						_lowerCAmelCase							=					PLBartTokenizer
						_lowerCAmelCase							=					None
						_lowerCAmelCase							=					False



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													super().setUp()

													# We have a SentencePiece fixture for testing
													__a		:		List[Any]									=       PLBartTokenizer(__A						,     language_codes="""base"""						,     keep_accents=__A							)
													tokenizer.save_pretrained(self.tmpdirname							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       PLBartTokenizer(__A						,     language_codes="""base"""						,     keep_accents=__A							)

													__a		:		Optional[int]									=       tokenizer.tokenize("""This is a test"""							)
													self.assertListEqual(__A						,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

													self.assertListEqual(
													    tokenizer.convert_tokens_to_ids(__A							)						,     [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]						,     )

													__a		:		Optional[Any]									=       tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
													self.assertListEqual(
													    __A						,     [
													        SPIECE_UNDERLINE + """I""",
													        SPIECE_UNDERLINE + """was""",
													        SPIECE_UNDERLINE + """b""",
													        """or""",
													        """n""",
													        SPIECE_UNDERLINE + """in""",
													        SPIECE_UNDERLINE + """""",
													        """9""",
													        """2""",
													        """0""",
													        """0""",
													        """0""",
													        """,""",
													        SPIECE_UNDERLINE + """and""",
													        SPIECE_UNDERLINE + """this""",
													        SPIECE_UNDERLINE + """is""",
													        SPIECE_UNDERLINE + """f""",
													        """al""",
													        """s""",
													        """é""",
													        """.""",
													    ]						,     )
													__a		:		Optional[int]									=       tokenizer.convert_tokens_to_ids(__A							)
													self.assertListEqual(
													    __A						,     [
													        value + tokenizer.fairseq_offset
													        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
													    ]						,     )

													__a		:		Union[str, Any]									=       tokenizer.convert_ids_to_tokens(__A							)
													self.assertListEqual(
													    __A						,     [
													        SPIECE_UNDERLINE + """I""",
													        SPIECE_UNDERLINE + """was""",
													        SPIECE_UNDERLINE + """b""",
													        """or""",
													        """n""",
													        SPIECE_UNDERLINE + """in""",
													        SPIECE_UNDERLINE + """""",
													        """<unk>""",
													        """2""",
													        """0""",
													        """0""",
													        """0""",
													        """,""",
													        SPIECE_UNDERLINE + """and""",
													        SPIECE_UNDERLINE + """this""",
													        SPIECE_UNDERLINE + """is""",
													        SPIECE_UNDERLINE + """f""",
													        """al""",
													        """s""",
													        """<unk>""",
													        """.""",
													    ]						,     )

													__a		:		int									=       tokenizer.vocab_size
													__a		:		Tuple									=       [tokenizer.convert_ids_to_tokens(__A							) for x in range(end - 4						,     __A							)]

													self.assertListEqual(__A						,     ["""__java__""", """__python__""", """__en_XX__""", """<mask>"""]							)

													__a		:		Tuple									=       "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
													__a		:		Dict									=       tokenizer(__A							).input_ids
													self.assertEqual(
													    tokenizer.decode(__A						,     skip_special_tokens=__A						,     clean_up_tokenization_spaces=__A							)						,     __A						,     )





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       PLBartTokenizer(__A						,     language_codes="""multi"""						,     keep_accents=__A							)

													__a		:		Optional[Any]									=       tokenizer.tokenize("""This is a test"""							)
													self.assertListEqual(__A						,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

													self.assertListEqual(
													    tokenizer.convert_tokens_to_ids(__A							)						,     [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]						,     )

													__a		:		Dict									=       tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
													self.assertListEqual(
													    __A						,     [
													        SPIECE_UNDERLINE + """I""",
													        SPIECE_UNDERLINE + """was""",
													        SPIECE_UNDERLINE + """b""",
													        """or""",
													        """n""",
													        SPIECE_UNDERLINE + """in""",
													        SPIECE_UNDERLINE + """""",
													        """9""",
													        """2""",
													        """0""",
													        """0""",
													        """0""",
													        """,""",
													        SPIECE_UNDERLINE + """and""",
													        SPIECE_UNDERLINE + """this""",
													        SPIECE_UNDERLINE + """is""",
													        SPIECE_UNDERLINE + """f""",
													        """al""",
													        """s""",
													        """é""",
													        """.""",
													    ]						,     )
													__a		:		Dict									=       tokenizer.convert_tokens_to_ids(__A							)
													self.assertListEqual(
													    __A						,     [
													        value + tokenizer.fairseq_offset
													        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
													    ]						,     )

													__a		:		Optional[Any]									=       tokenizer.convert_ids_to_tokens(__A							)
													self.assertListEqual(
													    __A						,     [
													        SPIECE_UNDERLINE + """I""",
													        SPIECE_UNDERLINE + """was""",
													        SPIECE_UNDERLINE + """b""",
													        """or""",
													        """n""",
													        SPIECE_UNDERLINE + """in""",
													        SPIECE_UNDERLINE + """""",
													        """<unk>""",
													        """2""",
													        """0""",
													        """0""",
													        """0""",
													        """,""",
													        SPIECE_UNDERLINE + """and""",
													        SPIECE_UNDERLINE + """this""",
													        SPIECE_UNDERLINE + """is""",
													        SPIECE_UNDERLINE + """f""",
													        """al""",
													        """s""",
													        """<unk>""",
													        """.""",
													    ]						,     )
													__a		:		List[Any]									=       tokenizer.vocab_size
													__a		:		Union[str, Any]									=       [tokenizer.convert_ids_to_tokens(__A							) for x in range(end - 7						,     __A							)]

													self.assertListEqual(
													    __A						,     ["""__java__""", """__python__""", """__en_XX__""", """__javascript__""", """__php__""", """__ruby__""", """__go__"""]							)
													__a		:		str									=       "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
													__a		:		List[Any]									=       tokenizer(__A							).input_ids
													self.assertEqual(
													    tokenizer.decode(__A						,     skip_special_tokens=__A						,     clean_up_tokenization_spaces=__A							)						,     __A						,     )







@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase	):
						_lowerCAmelCase							=					"uclanlp/plbart-python-en_XX"
						_lowerCAmelCase							=					[
						    "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])",
						    "def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])",
						]
						_lowerCAmelCase							=					[
						    "Returns the maximum value of a b c.",
						    "Sums the values of a b c.",
						]
						_lowerCAmelCase							=					[
						    1_3_4,
						    5_4_5_2,
						    3_3_4_6_0,
						    3_3_4_4_1,
						    3_3_4_6_3,
						    3_3_4_6_5,
						    3_3_4_6_3,
						    3_3_4_4_9,
						    9_8_8,
						    2_0,
						    3_3_4_5_6,
						    1_9,
						    3_3_4_5_6,
						    7_7_1,
						    3_9,
						    4_2_5_8,
						    8_8_9,
						    3_3_1_8,
						    3_3_4_4_1,
						    3_3_4_6_3,
						    3_3_4_6_5,
						    3_3_4_6_3,
						    3_3_4_4_9,
						    2_4_7_1,
						    2,
						    PYTHON_CODE,
						]



						@classmethod
						def     lowerCAmelCase__(cls							):





													'''simple docstring'''

													__a		:		PLBartTokenizer									=       PLBartTokenizer.from_pretrained(
													    cls.checkpoint_name						,     language_codes="""base"""						,     src_lang="""python"""						,     tgt_lang="""en_XX"""							)
													__a		:		Optional[Any]									=       1
													return cls



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__java__"""]						,     50001							)
													self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__python__"""]						,     50002							)
													self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__en_XX__"""]						,     50003							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
													self.assertListEqual(self.expected_src_tokens						,     __A							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													self.assertIn(__A						,     self.tokenizer.all_special_ids							)
													__a		:		List[str]									=       [EN_CODE, 9037, 33442, 57, 752, 153, 14, 56, 18, 9, 2]
													__a		:		List[str]									=       self.tokenizer.decode(__A						,     skip_special_tokens=__A							)
													__a		:		int									=       self.tokenizer.decode(generated_ids[1:]						,     skip_special_tokens=__A							)
													self.assertEqual(__A						,     __A							)
													self.assertNotIn(self.tokenizer.eos_token						,     __A							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       ["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
													self.assertIsInstance(src_text[0]						,     __A							)
													__a		:		Dict									=       10
													__a		:		List[Any]									=       self.tokenizer(__A						,     max_length=__A						,     truncation=__A							).input_ids[0]
													self.assertEqual(ids[-2]						,     2							)
													self.assertEqual(ids[-1]						,     __A							)
													self.assertEqual(len(__A							)						,     __A							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """__java__"""]							)						,     [50004, 50001]							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       tempfile.mkdtemp()
													__a		:		List[Any]									=       self.tokenizer.fairseq_tokens_to_ids
													self.tokenizer.save_pretrained(__A							)
													__a		:		List[Any]									=       PLBartTokenizer.from_pretrained(__A							)
													self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,     __A							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Any									=       self.tokenizer(self.src_text						,     text_target=self.tgt_text						,     padding=__A						,     return_tensors="""pt"""							)
													__a		:		int									=       shift_tokens_right(batch["""labels"""]						,     self.tokenizer.pad_token_id							)

													# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
													self.assertEqual(batch.input_ids[1][-2:].tolist()						,     [2, PYTHON_CODE]							)
													self.assertEqual(batch.decoder_input_ids[1][0]						,     __A							)
													self.assertEqual(batch.decoder_input_ids[1][-1]						,     2							)
													self.assertEqual(batch.labels[1][-2:].tolist()						,     [2, EN_CODE]							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       self.tokenizer(
													    self.src_text						,     text_target=self.tgt_text						,     padding=__A						,     truncation=__A						,     max_length=len(self.expected_src_tokens							)						,     return_tensors="""pt"""						,     )
													__a		:		Optional[Any]									=       shift_tokens_right(batch["""labels"""]						,     self.tokenizer.pad_token_id							)

													self.assertIsInstance(__A						,     __A							)

													self.assertEqual((2, 26)						,     batch.input_ids.shape							)
													self.assertEqual((2, 26)						,     batch.attention_mask.shape							)
													__a		:		Optional[int]									=       batch.input_ids.tolist()[0]
													self.assertListEqual(self.expected_src_tokens						,     __A							)
													self.assertEqual(2						,     batch.decoder_input_ids[0, -1]							)  # EOS
													# Test that special tokens are reset
													self.assertEqual(self.tokenizer.prefix_tokens						,     []							)
													self.assertEqual(self.tokenizer.suffix_tokens						,     [self.tokenizer.eos_token_id, PYTHON_CODE]							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[Any]									=       self.tokenizer(self.src_text						,     padding=__A						,     truncation=__A						,     max_length=3						,     return_tensors="""pt"""							)
													__a		:		str									=       self.tokenizer(
													    text_target=self.tgt_text						,     padding=__A						,     truncation=__A						,     max_length=10						,     return_tensors="""pt"""							)
													__a		:		int									=       targets["input_ids"]
													__a		:		Tuple									=       shift_tokens_right(__A						,     self.tokenizer.pad_token_id							)

													self.assertEqual(batch.input_ids.shape[1]						,     3							)
													self.assertEqual(batch.decoder_input_ids.shape[1]						,     10							)





						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       self.tokenizer._build_translation_inputs(
													    """A test"""						,     return_tensors="""pt"""						,     src_lang="""en_XX"""						,     tgt_lang="""java"""							)

													self.assertEqual(
													    nested_simplify(__A							)						,     {
													        # A, test, EOS, en_XX
													        """input_ids""": [[150, 242, 2, 50003]],
													        """attention_mask""": [[1, 1, 1, 1]],
													        # java
													        """forced_bos_token_id""": 50001,
													    }						,     )






"""simple docstring"""

import math
import sys

import cva
import numpy as np





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float					):
							# For applying gaussian function for each element in matrix.
							__a		:		int									=       math.sqrt(_lowerCamelCase					)
							__a		:		Any									=       1 / (sigma * math.sqrt(2 * math.pi					))
							return cons * np.exp(-((img / sigma) ** 2) * 0.5					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int					):
							__a		:		Any									=       kernel_size // 2
							return img[x - half : x + half + 1, y - half : y + half + 1]





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: float					):
							# Creates a gaussian kernel of given dimension.
							__a		:		int									=       np.zeros((kernel_size, kernel_size)					)
							for i in range(0  ,       _lowerCamelCase					):
														for j in range(0  ,       _lowerCamelCase					):
																					__a		:		Any									=       math.sqrt(
																					    abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
							return vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: int  ,       ):
							__a		:		Tuple									=       np.zeros(img.shape					)
							__a		:		Optional[int]									=       get_gauss_kernel(_lowerCamelCase  ,       _lowerCamelCase					)
							__a							,     __a		:		int									=       img.shape
							for i in range(kernel_size // 2  ,       size_x - kernel_size // 2					):
														for j in range(kernel_size // 2  ,       size_y - kernel_size // 2					):
																					__a		:		List[str]									=       get_slice(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       img_s - img_s[kernel_size // 2, kernel_size // 2]
																					__a		:		Optional[Any]									=       vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Optional[Any]									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Tuple									=       np.sum(_lowerCamelCase					) / np.sum(_lowerCamelCase					)
																					__a		:		Optional[Any]									=       val
							return imga





def       __magic_name__    (   _lowerCamelCase				: list					):
							__a		:		Optional[Any]									=       args[1] if args[1:] else """../image_data/lena.jpg"""
							__a		:		Union[str, Any]									=       float(args[2]					) if args[2:] else 1.0
							__a		:		Optional[int]									=       float(args[3]					) if args[3:] else 1.0
							if args[4:]:
														__a		:		Any									=       int(args[4]					)
														__a		:		Any									=       kernel_size + abs(kernel_size % 2 - 1					)
							else:
														__a		:		Optional[int]									=       5
							return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
	lowercase__   ,   lowercase__   ,   lowercase__   ,   lowercase__			   =  parse_args(sys.argv)
	lowercase__			   =  cva.imread(filename, 0)
	cva.imshow("input image", img)

	lowercase__			   =  img / 255
	lowercase__			   =  out.astype("float32")
	lowercase__			   =  bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
	lowercase__			   =  out * 255
	lowercase__			   =  np.uinta(out)
	cva.imshow("output image", out)
	cva.waitKey(0)
	cva.destroyAllWindows()

