code: 
  
  
  import unittest
  
  from transformers import ErnieMTokenizer
  from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  lowerCAmelCase__   	=				get_tests_dir('fixtures/spiece.model')
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  class   lowerCAmelCase__ (       snake_case_  ,					unittest.TestCase):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				ErnieMTokenizer
       __SCREAMING_SNAKE_CASE						    =				False
       __SCREAMING_SNAKE_CASE						    =				True
       __SCREAMING_SNAKE_CASE						    =				False
       __SCREAMING_SNAKE_CASE						    =				False
  
  
       def 				_lowerCamelCase				(		self)    ->       Dict:
              super().setUp()
  
              # We have a SentencePiece fixture for testing
              _A   : Dict       					=				ErnieMTokenizer(__lowerCamelCase				,		unk_token="<unk>"				,		pad_token="<pad>")
              tokenizer.save_pretrained(self.tmpdirname)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       str:
              _A   : List[Any]       					=				"this is a test"
              _A   : Optional[int]       					=				"this is a test"
              return input_text, output_text
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              _A   : Dict       					=				"<pad>"
              _A   : Any       					=				0
  
              self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase)				,		__lowerCamelCase)
              self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase)				,		__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              _A   : int       					=				list(self.get_tokenizer().get_vocab().keys())
  
              self.assertEqual(vocab_keys[0]				,		"<pad>")
              self.assertEqual(vocab_keys[1]				,		"<unk>")
              self.assertEqual(vocab_keys[-1]				,		"▁eloquent")
              self.assertEqual(len(__lowerCamelCase)				,		3_0_0_0_0)
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[Any]:
              self.assertEqual(self.get_tokenizer().vocab_size				,		3_0_0_0_0)
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              if not self.test_rust_tokenizer:
                     return
  
              _A   : List[str]       					=				self.get_tokenizer()
              _A   : Union[str, Any]       					=				self.get_rust_tokenizer()
  
              _A   : Dict       					=				"I was born in 92000, and this is falsé."
  
              _A   : Tuple       					=				tokenizer.tokenize(__lowerCamelCase)
              _A   : Union[str, Any]       					=				rust_tokenizer.tokenize(__lowerCamelCase)
              self.assertListEqual(__lowerCamelCase				,		__lowerCamelCase)
  
              _A   : Any       					=				tokenizer.encode(__lowerCamelCase				,		add_special_tokens=__lowerCamelCase)
              _A   : str       					=				rust_tokenizer.encode(__lowerCamelCase				,		add_special_tokens=__lowerCamelCase)
              self.assertListEqual(__lowerCamelCase				,		__lowerCamelCase)
  
              _A   : Any       					=				self.get_rust_tokenizer()
              _A   : List[str]       					=				tokenizer.encode(__lowerCamelCase)
              _A   : Tuple       					=				rust_tokenizer.encode(__lowerCamelCase)
              self.assertListEqual(__lowerCamelCase				,		__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              _A   : Dict       					=				ErnieMTokenizer(__lowerCamelCase				,		do_lower_case=__lowerCamelCase				,		unk_token="<unk>"				,		pad_token="<pad>")
  
              _A   : List[str]       					=				tokenizer.tokenize("This is a test")
              self.assertListEqual(__lowerCamelCase				,		["▁this", "▁is", "▁a", "▁test"])
  
              self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase)				,		[4_8, 2_5, 2_1, 1_2_8_9])
  
              _A   : int       					=				tokenizer.tokenize("I was born in 92000, and this is falsé.")
              # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
              self.assertListEqual(
                  __lowerCamelCase				,		["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."])
              _A   : List[str]       					=				tokenizer.convert_tokens_to_ids(__lowerCamelCase)
              self.assertListEqual(__lowerCamelCase				,		[3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9])
  
              _A   : Dict       					=				tokenizer.convert_ids_to_tokens(__lowerCamelCase)
              self.assertListEqual(
                  __lowerCamelCase				,		["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]				,		)
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              _A   : Optional[Any]       					=				ErnieMTokenizer(__lowerCamelCase				,		unk_token="<unk>"				,		pad_token="<pad>")
  
              _A   : List[Any]       					=				tokenizer.encode("sequence builders")
              _A   : Union[str, Any]       					=				tokenizer.encode("multi-sequence build")
  
              _A   : int       					=				tokenizer.build_inputs_with_special_tokens(__lowerCamelCase)
              _A   : Optional[Any]       					=				tokenizer.build_inputs_with_special_tokens(__lowerCamelCase				,		__lowerCamelCase)
  
              assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
              assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
                  tokenizer.sep_token_id
              ] + text_a + [tokenizer.sep_token_id]
  
  
       @slow
       def 				_lowerCamelCase				(		self)    ->       Optional[Any]:
              # fmt: off
              _A   : Tuple       					=				{"input_ids": [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 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], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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, 1, 1, 1]]}
              # fmt: on
  
              self.tokenizer_integration_test_util(
                  expected_encoding=__lowerCamelCase				,		model_name="susnato/ernie-m-base_pytorch"				,		sequences=[
                      "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
                      "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
                      "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
                      "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
                      "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
                      "conditioning on both left and right context in all layers.",
                      "The quick brown fox jumps over the lazy dog.",
                  ]				,		)
style_context: 
  
  
  import cva
  import numpy as np
  
  from digital_image_processing.filters.convolve import img_convolve
  from digital_image_processing.filters.sobel_filter import sobel_filter
  
  lowerCAmelCase__   	=				1_80
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]		):
         _A   : int       					=				k_size // 2
         _A					,    _A   : Tuple       					=				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
         _A   : Union[str, Any]       					=				(
             1
             / (2 * np.pi * sigma)
             * np.exp(-(np.square(UpperCamelCase__		) + np.square(UpperCamelCase__		)) / (2 * np.square(UpperCamelCase__		))		)
         )
         return g
  def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Tuple		):
         _A   : Optional[int]       					=				np.zeros(UpperCamelCase__		)
  
         for row in range(1       ,							image_shape[0] - 1		):
                for col in range(1       ,							image_shape[1] - 1		):
                       _A   : Dict       					=				gradient_direction[row, col]
  
                       if (
                           0 <= direction < PI / 8
                           or 15 * PI / 8 <= direction <= 2 * PI
                           or 7 * PI / 8 <= direction <= 9 * PI / 8
                       ):
                              _A   : Dict       					=				sobel_grad[row, col - 1]
                              _A   : Any       					=				sobel_grad[row, col + 1]
                              if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                     _A   : List[Any]       					=				sobel_grad[row, col]
  
                       elif (
                           PI / 8 <= direction < 3 * PI / 8
                           or 9 * PI / 8 <= direction < 11 * PI / 8
                       ):
                              _A   : List[str]       					=				sobel_grad[row + 1, col - 1]
                              _A   : str       					=				sobel_grad[row - 1, col + 1]
                              if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                     _A   : List[Any]       					=				sobel_grad[row, col]
  
                       elif (
                           3 * PI / 8 <= direction < 5 * PI / 8
                           or 11 * PI / 8 <= direction < 13 * PI / 8
                       ):
                              _A   : Any       					=				sobel_grad[row - 1, col]
                              _A   : int       					=				sobel_grad[row + 1, col]
                              if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                     _A   : Any       					=				sobel_grad[row, col]
  
                       elif (
                           5 * PI / 8 <= direction < 7 * PI / 8
                           or 13 * PI / 8 <= direction < 15 * PI / 8
                       ):
                              _A   : List[str]       					=				sobel_grad[row - 1, col - 1]
                              _A   : Optional[Any]       					=				sobel_grad[row + 1, col + 1]
                              if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                     _A   : List[Any]       					=				sobel_grad[row, col]
  
         return destination
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Any		):
         for row in range(1       ,							image_shape[0] - 1		):
                for col in range(1       ,							image_shape[1] - 1		):
                       if destination[row, col] >= threshold_high:
                              _A   : Optional[int]       					=				strong
                       elif destination[row, col] <= threshold_low:
                              _A   : Optional[int]       					=				0
                       else:
                              _A   : Union[str, Any]       					=				weak
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Any		):
         for row in range(1       ,							image_shape[0]		):
                for col in range(1       ,							image_shape[1]		):
                       if destination[row, col] == weak:
                              if 255 in (
                                  destination[row, col + 1],
                                  destination[row, col - 1],
                                  destination[row - 1, col],
                                  destination[row + 1, col],
                                  destination[row - 1, col - 1],
                                  destination[row + 1, col - 1],
                                  destination[row - 1, col + 1],
                                  destination[row + 1, col + 1],
                              ):
                                     _A   : str       					=				strong
                              else:
                                     _A   : Any       					=				0
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						int=15       ,							UpperCamelCase__  :						Dict=30       ,							UpperCamelCase__  :						int=128       ,							UpperCamelCase__  :						Union[str, Any]=255		):
         # gaussian_filter
         _A   : Union[str, Any]       					=				img_convolve(UpperCamelCase__       ,							gen_gaussian_kernel(9       ,							sigma=1.4		)		)
         # get the gradient and degree by sobel_filter
         _A					,    _A   : Dict       					=				sobel_filter(UpperCamelCase__		)
         _A   : Tuple       					=				PI + np.radadeg(UpperCamelCase__		)
  
         _A   : Optional[int]       					=				suppress_non_maximum(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         detect_high_low_threshold(
             image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         track_edge(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         return destination
  
  
  if __name__ == "__main__":
      # read original image in gray mode
      lowerCAmelCase__   	=				cva.imread(R'../image_data/lena.jpg', 0)
      # canny edge detection
      lowerCAmelCase__   	=				canny(lena)
      cva.imshow('canny', canny_destination)
      cva.waitKey(0)
  