import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A_      =      get_tests_dir("fixtures/test_sentencepiece_no_bos.model")






@require_sentencepiece
@require_tokenizers
class      __lowercase					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     PegasusTokenizer
						lowercase					 =     PegasusTokenizerFast
						lowercase					 =     True
						lowercase					 =     True

						def        __a		(	self				:      Tuple   )							->		List[str]:


									'''simple docstring'''

									super().setUp()

									# We have a SentencePiece fixture for testing
									lowercase							=		PegasusTokenizer(__lowerCamelCase   )
									tokenizer.save_pretrained(self.tmpdirname   )

						@cached_property
						def        __a		(	self				:      List[str]   )							->		List[Any]:


									'''simple docstring'''

									return PegasusTokenizer.from_pretrained('''google/pegasus-large'''   )

						def        __a		(	self				:      Any							,							**__lowerCamelCase				:      Union[str, Any]   )							->		PegasusTokenizer:


									'''simple docstring'''

									return PegasusTokenizer.from_pretrained(self.tmpdirname							,							**__lowerCamelCase   )

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Dict   )							->		Any:


									'''simple docstring'''

									return ("This is a test", "This is a test")

						def        __a		(	self				:      List[Any]   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		'''</s>'''
									lowercase							=		1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase   )							,							__lowerCamelCase   )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase   )							,							__lowerCamelCase   )

						def        __a		(	self				:      Optional[int]   )							->		int:


									'''simple docstring'''

									lowercase							=		list(self.get_tokenizer().get_vocab().keys()   )

									self.assertEqual(vocab_keys[0]							,							'''<pad>'''   )
									self.assertEqual(vocab_keys[1]							,							'''</s>'''   )
									self.assertEqual(vocab_keys[-1]							,							'''v'''   )
									self.assertEqual(len(__lowerCamelCase   )							,							11_03   )

						def        __a		(	self				:      str   )							->		Optional[int]:


									'''simple docstring'''

									self.assertEqual(self.get_tokenizer().vocab_size							,							11_03   )

						def        __a		(	self				:      Any   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
									lowercase							=		self.tokenizer_class.from_pretrained(self.tmpdirname   )
									lowercase							=		(
									    '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
									    ''' </s> <pad> <pad> <pad>'''
									)
									lowercase							=		rust_tokenizer([raw_input_str]							,							return_tensors=__lowerCamelCase							,							add_special_tokens=__lowerCamelCase   ).input_ids[0]
									lowercase							=		py_tokenizer([raw_input_str]							,							return_tensors=__lowerCamelCase							,							add_special_tokens=__lowerCamelCase   ).input_ids[0]
									self.assertListEqual(__lowerCamelCase							,							__lowerCamelCase   )

						def        __a		(	self				:      Union[str, Any]   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		self._large_tokenizer
									# <mask_1> masks whole sentence while <mask_2> masks single word
									lowercase							=		'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
									lowercase							=		[2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
									lowercase							=		tokenizer([raw_input_str]							,							return_tensors=__lowerCamelCase   ).input_ids[0]
									self.assertListEqual(__lowerCamelCase							,							__lowerCamelCase   )

						def        __a		(	self				:      str   )							->		Any:


									'''simple docstring'''

									lowercase							=		self._large_tokenizer
									# The tracebacks for the following asserts are **better** without messages or self.assertEqual
									assert tokenizer.vocab_size == 9_61_03
									assert tokenizer.pad_token_id == 0
									assert tokenizer.eos_token_id == 1
									assert tokenizer.offset == 1_03
									assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
									assert tokenizer.unk_token == "<unk>"
									assert tokenizer.model_max_length == 10_24
									lowercase							=		'''To ensure a smooth flow of bank resolutions.'''
									lowercase							=		[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
									lowercase							=		tokenizer([raw_input_str]							,							return_tensors=__lowerCamelCase   ).input_ids[0]
									self.assertListEqual(__lowerCamelCase							,							__lowerCamelCase   )
									assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]   ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

						@require_torch
						def        __a		(	self				:      Tuple   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		['''This is going to be way too long.''' * 1_50, '''short example''']
									lowercase							=		['''not super long but more than 5 tokens''', '''tiny''']
									lowercase							=		self._large_tokenizer(__lowerCamelCase							,							padding=__lowerCamelCase							,							truncation=__lowerCamelCase							,							return_tensors='''pt'''   )
									lowercase							=		self._large_tokenizer(
									    text_target=__lowerCamelCase							,							max_length=5							,							padding=__lowerCamelCase							,							truncation=__lowerCamelCase							,							return_tensors='''pt'''   )

									assert batch.input_ids.shape == (2, 10_24)
									assert batch.attention_mask.shape == (2, 10_24)
									assert targets["input_ids"].shape == (2, 5)
									assert len(__lowerCamelCase   ) == 2  # input_ids, attention_mask.



						@slow
						def        __a		(	self				:      Optional[int]   )							->		int:


									'''simple docstring'''

									lowercase							=		{'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=__lowerCamelCase							,							model_name='''google/bigbird-pegasus-large-arxiv'''							,							revision='''ba85d0851d708441f91440d509690f1ab6353415'''							,							)






@require_sentencepiece
@require_tokenizers
class      __lowercase					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     PegasusTokenizer
						lowercase					 =     PegasusTokenizerFast
						lowercase					 =     True
						lowercase					 =     True

						def        __a		(	self				:      str   )							->		List[str]:


									'''simple docstring'''

									super().setUp()

									# We have a SentencePiece fixture for testing
									lowercase							=		PegasusTokenizer(__lowerCamelCase							,							offset=0							,							mask_token_sent=__lowerCamelCase							,							mask_token='''[MASK]'''   )
									tokenizer.save_pretrained(self.tmpdirname   )

						@cached_property
						def        __a		(	self				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''   )

						def        __a		(	self				:      Optional[Any]							,							**__lowerCamelCase				:      Any   )							->		PegasusTokenizer:


									'''simple docstring'''

									return PegasusTokenizer.from_pretrained(self.tmpdirname							,							**__lowerCamelCase   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[int]   )							->		Any:


									'''simple docstring'''

									return ("This is a test", "This is a test")

						def        __a		(	self				:      List[Any]   )							->		Dict:


									'''simple docstring'''

									lowercase							=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
									lowercase							=		self.tokenizer_class.from_pretrained(self.tmpdirname   )
									lowercase							=		(
									    '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
									    ''' <pad> <pad> <pad>'''
									)
									lowercase							=		rust_tokenizer([raw_input_str]							,							return_tensors=__lowerCamelCase							,							add_special_tokens=__lowerCamelCase   ).input_ids[0]
									lowercase							=		py_tokenizer([raw_input_str]							,							return_tensors=__lowerCamelCase							,							add_special_tokens=__lowerCamelCase   ).input_ids[0]
									self.assertListEqual(__lowerCamelCase							,							__lowerCamelCase   )

						@require_torch
						def        __a		(	self				:      List[str]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		['''This is going to be way too long.''' * 10_00, '''short example''']
									lowercase							=		['''not super long but more than 5 tokens''', '''tiny''']
									lowercase							=		self._large_tokenizer(__lowerCamelCase							,							padding=__lowerCamelCase							,							truncation=__lowerCamelCase							,							return_tensors='''pt'''   )
									lowercase							=		self._large_tokenizer(
									    text_target=__lowerCamelCase							,							max_length=5							,							padding=__lowerCamelCase							,							truncation=__lowerCamelCase							,							return_tensors='''pt'''   )

									assert batch.input_ids.shape == (2, 40_96)
									assert batch.attention_mask.shape == (2, 40_96)
									assert targets["input_ids"].shape == (2, 5)
									assert len(__lowerCamelCase   ) == 2  # input_ids, attention_mask.



						def        __a		(	self				:      int   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		(
									    '''This is an example string that is used to test the original TF implementation against the HF'''
									    ''' implementation'''
									)

									lowercase							=		self._large_tokenizer(__lowerCamelCase   ).input_ids

									self.assertListEqual(
									    __lowerCamelCase							,							[1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]							,							)






import math
import sys

import cva
import numpy as np






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		math.sqrt(UpperCAmelCase       )
			lowercase							=		1 / (sigma * math.sqrt(2 * math.pi       ))
			return cons * np.exp(-((img / sigma) ** 2) * 0.5       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		kernel_size // 2
			return img[x - half : x + half + 1, y - half : y + half + 1]






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros((kernel_size, kernel_size)       )
			for i in range(0,		UpperCAmelCase       ):
						for j in range(0,		UpperCAmelCase       ):
									lowercase							=		math.sqrt(
									    abs(i - kernel_size // 2       ) ** 2 + abs(j - kernel_size // 2       ) ** 2       )
			return vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		)->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros(img.shape       )
			lowercase							=		get_gauss_kernel(UpperCAmelCase,		UpperCAmelCase       )
			lowercase		,lowercase							=		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       ):
									lowercase							=		get_slice(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		img_s - img_s[kernel_size // 2, kernel_size // 2]
									lowercase							=		vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.sum(UpperCAmelCase       ) / np.sum(UpperCAmelCase       )
									lowercase							=		val
			return imga






def        __UpperCAmelCase    (       UpperCAmelCase       )->       tuple:



			"""simple docstring"""







			lowercase							=		args[1] if args[1:] else '''../image_data/lena.jpg'''
			lowercase							=		float(args[2]       ) if args[2:] else 1.0
			lowercase							=		float(args[3]       ) if args[3:] else 1.0
			if args[4:]:
						lowercase							=		int(args[4]       )
						lowercase							=		kernel_size + abs(kernel_size % 2 - 1       )
			else:
						lowercase							=		5
			return filename, spatial_variance, intensity_variance, kernel_size


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

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





