"""simple docstring"""




import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
     from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
     _a      :		str           =   get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
     from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

_a      :		str           =   128_022
_a      :		Optional[Any]           =   128_028
@require_sentencepiece
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Optional[Any]							=		MaMaaaTokenizer
     _UpperCamelCase							:			int							=		False
     _UpperCamelCase							:			int							=		False
     _UpperCamelCase							:			int							=		True






     def     __A  (		self  ):
       super().setUp()

       _lowerCAmelCase						:       Optional[Any]  			=     ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
       _lowerCAmelCase						:       Dict  			=     dict(zip(a__				,     range(len(a__  )  )  )  )
       _lowerCAmelCase						:       List[Any]  			=     Path(self.tmpdirname  )
       save_json(a__				,     save_dir / VOCAB_FILES_NAMES["""vocab_file"""]  )
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
         copyfile(a__				,     save_dir / VOCAB_FILES_NAMES["""spm_file"""]  )

       _lowerCAmelCase						:       Any  			=     MaMaaaTokenizer.from_pretrained(self.tmpdirname  )
       tokenizer.save_pretrained(self.tmpdirname  )






     def     __A  (		self				,     **a__  ):
       return MaMaaaTokenizer.from_pretrained(self.tmpdirname				,     **a__  )






     def     __A  (		self				,     a__  ):
       return (
           "This is a test",
           "This is a test",
       )






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     """</s>"""
       _lowerCAmelCase						:       Optional[Any]  			=     0

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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.get_tokenizer()
       _lowerCAmelCase						:       Any  			=     list(tokenizer.get_vocab().keys()  )

       self.assertEqual(vocab_keys[0]				,     """</s>"""  )
       self.assertEqual(vocab_keys[1]				,     """<unk>"""  )
       self.assertEqual(vocab_keys[-1]				,     """<s>"""  )
       self.assertEqual(len(a__  )				,     tokenizer.vocab_size + len(tokenizer.get_added_vocab()  )  )






     @unittest.skip("""Skip this test while all models are still to be uploaded."""  )
     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.get_tokenizer()

       _lowerCAmelCase						:       Tuple  			=     tokenizer.tokenize("""This is a test"""  )
       self.assertListEqual(a__				,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]  )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(a__  )				,     [2, 3, 4, 5, 6]				,     )

       _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]  )
       self.assertListEqual(a__				,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]  )

       _lowerCAmelCase						:       int  			=     tokenizer.convert_tokens_to_string(a__  )
       self.assertEqual(a__				,     """This is a test"""  )






     @slow
     def     __A  (		self  ):
       # fmt: off
       _lowerCAmelCase						:       Optional[int]  			=     {"""input_ids""": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 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]], """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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 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=a__				,     model_name="""facebook/m2m100_418M"""				,     revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""				,     )







@require_torch
@require_sentencepiece
@require_tokenizers
class 						__A							( unittest.TestCase		):
     _UpperCamelCase							:			Union[str, Any]							=		"facebook/m2m100_418M"
     _UpperCamelCase							:			Optional[Any]							=		[
         "In my opinion, there are two levels of response from the French government.",
         "NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
     ]
     _UpperCamelCase							:			Optional[int]							=		[
         "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
         "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
     ]

     # fmt: off
     _UpperCamelCase							:			List[str]							=		[EN_CODE, 593, 1_949, 115_781, 4, 71_586, 4_234, 60_633, 126_233, 432, 123_808, 15_592, 1_197, 117_132, 120_618, 5, 2]






     @classmethod
     def     __A  (		cls  ):
       _lowerCAmelCase						:       MaMaaaTokenizer  			=     MaMaaaTokenizer.from_pretrained(
           cls.checkpoint_name				,     src_lang="""en"""				,     tgt_lang="""fr"""  )
       _lowerCAmelCase						:       Tuple  			=     1
       return cls






     def     __A  (		self  ):
       self.assertEqual(self.tokenizer.get_lang_id("""ar"""  )				,     128006  )
       self.assertEqual(self.tokenizer.get_lang_id("""en"""  )				,     128022  )
       self.assertEqual(self.tokenizer.get_lang_id("""ro"""  )				,     128076  )
       self.assertEqual(self.tokenizer.get_lang_id("""mr"""  )				,     128063  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.tokenizer.get_vocab()
       self.assertEqual(len(a__  )				,     self.tokenizer.vocab_size  )
       self.assertEqual(vocab["""<unk>"""]				,     3  )
       self.assertIn(self.tokenizer.get_lang_token("""en"""  )				,     a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     """en"""
       _lowerCAmelCase						:       int  			=     self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
       self.assertListEqual(self.expected_src_tokens				,     a__  )






     def     __A  (		self  ):
       self.assertIn(a__				,     self.tokenizer.all_special_ids  )
       # fmt: off
       _lowerCAmelCase						:       Union[str, Any]  			=     [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
       # fmt: on
       _lowerCAmelCase						:       str  			=     self.tokenizer.decode(a__				,     skip_special_tokens=a__  )
       _lowerCAmelCase						:       List[Any]  			=     self.tokenizer.decode(generated_ids[1:]				,     skip_special_tokens=a__  )
       self.assertEqual(a__				,     a__  )
       self.assertNotIn(self.tokenizer.eos_token				,     a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     tempfile.mkdtemp()
       _lowerCAmelCase						:       Tuple  			=     self.tokenizer.lang_token_to_id
       self.tokenizer.save_pretrained(a__  )
       _lowerCAmelCase						:       Tuple  			=     MaMaaaTokenizer.from_pretrained(a__  )
       self.assertDictEqual(new_tok.lang_token_to_id				,     a__  )






     @require_torch
     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     """en"""
       _lowerCAmelCase						:       int  			=     """fr"""

       _lowerCAmelCase						:       Any  			=     self.tokenizer(self.src_text				,     text_target=self.tgt_text				,     padding=a__				,     return_tensors="""pt"""  )

       _lowerCAmelCase						:       int  			=     shift_tokens_right(
           batch["""labels"""]				,     self.tokenizer.pad_token_id				,     self.tokenizer.eos_token_id  )

       for k in batch:
         _lowerCAmelCase						:       Any  			=     batch[k].tolist()
       # batch = {k: v.tolist() for k,v in batch.items()}
       # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
       # batch.decoder_inputs_ids[0][0] ==
       assert batch.input_ids[1][0] == EN_CODE
       assert batch.input_ids[1][-1] == 2
       assert batch.labels[1][0] == FR_CODE
       assert batch.labels[1][-1] == 2
       assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]






     @require_torch
     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     """mr"""
       self.assertListEqual(self.tokenizer.prefix_tokens				,     [self.tokenizer.get_lang_id("""mr"""  )]  )
       self.assertListEqual(self.tokenizer.suffix_tokens				,     [self.tokenizer.eos_token_id]  )

       _lowerCAmelCase						:       List[Any]  			=     """zh"""
       self.assertListEqual(self.tokenizer.prefix_tokens				,     [self.tokenizer.get_lang_id("""zh"""  )]  )
       self.assertListEqual(self.tokenizer.suffix_tokens				,     [self.tokenizer.eos_token_id]  )






     @require_torch
     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     """mr"""
       self.tokenizer._switch_to_target_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens				,     [self.tokenizer.get_lang_id("""mr"""  )]  )
       self.assertListEqual(self.tokenizer.suffix_tokens				,     [self.tokenizer.eos_token_id]  )
       self.tokenizer._switch_to_input_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens				,     [self.tokenizer.get_lang_id(self.tokenizer.src_lang  )]  )

       _lowerCAmelCase						:       Union[str, Any]  			=     """zh"""
       self.tokenizer._switch_to_target_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens				,     [self.tokenizer.get_lang_id("""zh"""  )]  )
       self.assertListEqual(self.tokenizer.suffix_tokens				,     [self.tokenizer.eos_token_id]  )
       self.tokenizer._switch_to_input_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens				,     [self.tokenizer.get_lang_id(self.tokenizer.src_lang  )]  )






     @require_torch
     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.tokenizer._build_translation_inputs("""A test"""				,     return_tensors="""pt"""				,     src_lang="""en"""				,     tgt_lang="""ar"""  )

       self.assertEqual(
           nested_simplify(a__  )				,     {
               # en_XX, A, test, EOS
               """input_ids""": [[128022, 58, 4183, 2]],
               """attention_mask""": [[1, 1, 1, 1]],
               # ar_AR
               """forced_bos_token_id""": 128006,
           }				,     )





"""simple docstring"""




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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       int	)       ->      List[str]:
  _lowerCAmelCase						:       Tuple  			=     k_size // 2
  _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _lowerCAmelCase						:       Union[str, Any]  			=     1 / (2 * pi * sigma) * exp(-(square(_lowerCamelCase	) + square(_lowerCamelCase	)) / (2 * square(_lowerCamelCase	))	)
  return g





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict			,_lowerCamelCase						:       int			,_lowerCamelCase						:       int	)       ->      Dict:
  _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     image.shape[0], image.shape[1]
  # dst image height and width
  _lowerCAmelCase						:       Optional[int]  			=     height - k_size + 1
  _lowerCAmelCase						:       Dict  			=     width - k_size + 1

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

  #  turn the kernel into shape(k*k, 1)
  _lowerCAmelCase						:       List[Any]  			=     gen_gaussian_kernel(_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     ravel(_lowerCamelCase	)

  # reshape and get the dst image
  _lowerCAmelCase						:       int  			=     dot(_lowerCamelCase			,_lowerCamelCase	).reshape(_lowerCamelCase			,_lowerCamelCase	).astype(_lowerCamelCase	)

  return dst


if __name__ == "__main__":
     # read original image
     _a      :		Optional[Any]           =   imread(r'../image_data/lena.jpg')
     # turn image in gray scale value
     _a      :		Dict           =   cvtColor(img, COLOR_BGR2GRAY)

     # get values with two different mask size
     _a      :		Union[str, Any]           =   gaussian_filter(gray, 3, sigma=1)
     _a      :		List[Any]           =   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()




