

"""simple docstring"""




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


SCREAMING_SNAKE_CASE_  :      Optional[Any]							=      get_tests_dir('''fixtures/spiece.model''')


@require_sentencepiece
@require_tokenizers
class 		_A							(      __a					,							unittest.TestCase       ):
      __a             =						ErnieMTokenizer
      __a             =						False
      __a             =						True
      __a             =						False
      __a             =						False





      def        _lowerCamelCase	(	self		) ->       List[str]:
         super().setUp()

         # We have a SentencePiece fixture for testing
         lowerCamelCase__						       =      ErnieMTokenizer(SCREAMING_SNAKE_CASE__     ,    unk_token="<unk>"     ,    pad_token="<pad>"		)
         tokenizer.save_pretrained(self.tmpdirname		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       int:
         lowerCamelCase__						       =      "this is a test"
         lowerCamelCase__						       =      "this is a test"
         return input_text, output_text





      def        _lowerCamelCase	(	self		) ->       int:
         lowerCamelCase__						       =      "<pad>"
         lowerCamelCase__						       =      0

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





      def        _lowerCamelCase	(	self		) ->       List[Any]:
         lowerCamelCase__						       =      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(SCREAMING_SNAKE_CASE__		)     ,    30000		)





      def        _lowerCamelCase	(	self		) ->       str:
         self.assertEqual(self.get_tokenizer().vocab_size     ,    30000		)





      def        _lowerCamelCase	(	self		) ->       Any:
         if not self.test_rust_tokenizer:
            return

         lowerCamelCase__						       =      self.get_tokenizer()
         lowerCamelCase__						       =      self.get_rust_tokenizer()

         lowerCamelCase__						       =      "I was born in 92000, and this is falsé."

         lowerCamelCase__						       =      tokenizer.tokenize(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__		)
         self.assertListEqual(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      tokenizer.encode(SCREAMING_SNAKE_CASE__     ,    add_special_tokens=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     ,    add_special_tokens=SCREAMING_SNAKE_CASE__		)
         self.assertListEqual(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      self.get_rust_tokenizer()
         lowerCamelCase__						       =      tokenizer.encode(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      rust_tokenizer.encode(SCREAMING_SNAKE_CASE__		)
         self.assertListEqual(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self		) ->       List[Any]:
         lowerCamelCase__						       =      ErnieMTokenizer(SCREAMING_SNAKE_CASE__     ,    do_lower_case=SCREAMING_SNAKE_CASE__     ,    unk_token="<unk>"     ,    pad_token="<pad>"		)

         lowerCamelCase__						       =      tokenizer.tokenize("This is a test"		)
         self.assertListEqual(SCREAMING_SNAKE_CASE__     ,    ["▁this", "▁is", "▁a", "▁test"]		)

         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__		)     ,    [48, 25, 21, 1289]		)

         lowerCamelCase__						       =      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(
             SCREAMING_SNAKE_CASE__     ,    ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]		)
         lowerCamelCase__						       =      tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__		)
         self.assertListEqual(SCREAMING_SNAKE_CASE__     ,    [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]		)

         lowerCamelCase__						       =      tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__		)
         self.assertListEqual(
             SCREAMING_SNAKE_CASE__     ,    ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]     ,    )





      def        _lowerCamelCase	(	self		) ->       Optional[int]:
         lowerCamelCase__						       =      ErnieMTokenizer(SCREAMING_SNAKE_CASE__     ,    unk_token="<unk>"     ,    pad_token="<pad>"		)

         lowerCamelCase__						       =      tokenizer.encode("sequence builders"		)
         lowerCamelCase__						       =      tokenizer.encode("multi-sequence build"		)

         lowerCamelCase__						       =      tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

         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[int]:
         # fmt: off
         lowerCamelCase__						       =      {"input_ids": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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=SCREAMING_SNAKE_CASE__     ,    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.",
             ]     ,    )







"""simple docstring"""




from __future__ import annotations



def 						UpperCAmelCase__ (					A__						,     A__						,     A__						,     A__ )   ->   None:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      m - 1, 0
   while i >= 0 and j < n:
      if key == mat[i][j]:
         print(f'Key {key} found at row- {i + 1} column- {j + 1}' )
         return
      if key < mat[i][j]:
         i -= 1
      else:
         j += 1
   print(f'Key {key} not found' )



def 						UpperCAmelCase__ (					)   ->   None:




   """simple docstring"""
   lowerCamelCase__						       =      [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
   lowerCamelCase__						       =      int(input("Enter the element to be searched:" ) )
   print(A__ )
   search_in_a_sorted_matrix(A__						,     len(A__ )						,     len(mat[0] )						,     A__ )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




