

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


a	: Optional[Any]      =				get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class 					a					(				snake_case_							, unittest.TestCase     ):



      """simple docstring"""


      a						:				Tuple							 =			ErnieMTokenizer
      a						:				Any							 =			False
      a						:				Any							 =			True
      a						:				Optional[Any]							 =			False
      a						:				Dict							 =			False
      def 			UpperCAmelCase   (   self      :    str      )							->   Dict:
        super().setUp()

        # We have a SentencePiece fixture for testing
        __UpperCAmelCase							:			List[Any]     =						ErnieMTokenizer(__lowercase	,   unk_token="""<unk>"""	,   pad_token="""<pad>"""      )
        tokenizer.save_pretrained(self.tmpdirname      )
      def 			UpperCAmelCase   (   self      :    Tuple	,   __lowercase      :    int      )							->   str:
        __UpperCAmelCase							:			str     =						"""this is a test"""
        __UpperCAmelCase							:			List[str]     =						"""this is a test"""
        return input_text, output_text
      def 			UpperCAmelCase   (   self      :    List[Any]      )							->   Optional[int]:
        __UpperCAmelCase							:			str     =						"""<pad>"""
        __UpperCAmelCase							:			Optional[int]     =						0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase      )	,   __lowercase      )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase      )	,   __lowercase      )
      def 			UpperCAmelCase   (   self      :    List[str]      )							->   Union[str, Any]:
        __UpperCAmelCase							:			Any     =						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(__lowercase      )	,   30000      )
      def 			UpperCAmelCase   (   self      :    Dict      )							->   Optional[Any]:
        self.assertEqual(self.get_tokenizer().vocab_size	,   30000      )
      def 			UpperCAmelCase   (   self      :    Any      )							->   str:
        if not self.test_rust_tokenizer:
          return

        __UpperCAmelCase							:			int     =						self.get_tokenizer()
        __UpperCAmelCase							:			Optional[Any]     =						self.get_rust_tokenizer()

        __UpperCAmelCase							:			List[str]     =						"""I was born in 92000, and this is falsé."""

        __UpperCAmelCase							:			str     =						tokenizer.tokenize(__lowercase      )
        __UpperCAmelCase							:			Tuple     =						rust_tokenizer.tokenize(__lowercase      )
        self.assertListEqual(__lowercase	,   __lowercase      )

        __UpperCAmelCase							:			Optional[int]     =						tokenizer.encode(__lowercase	,   add_special_tokens=__lowercase      )
        __UpperCAmelCase							:			int     =						rust_tokenizer.encode(__lowercase	,   add_special_tokens=__lowercase      )
        self.assertListEqual(__lowercase	,   __lowercase      )

        __UpperCAmelCase							:			Any     =						self.get_rust_tokenizer()
        __UpperCAmelCase							:			Tuple     =						tokenizer.encode(__lowercase      )
        __UpperCAmelCase							:			List[Any]     =						rust_tokenizer.encode(__lowercase      )
        self.assertListEqual(__lowercase	,   __lowercase      )
      def 			UpperCAmelCase   (   self      :    Optional[int]      )							->   Union[str, Any]:
        __UpperCAmelCase							:			Any     =						ErnieMTokenizer(__lowercase	,   do_lower_case=__lowercase	,   unk_token="""<unk>"""	,   pad_token="""<pad>"""      )

        __UpperCAmelCase							:			Dict     =						tokenizer.tokenize("""This is a test"""      )
        self.assertListEqual(__lowercase	,   ["""▁this""", """▁is""", """▁a""", """▁test"""]      )

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

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

        __UpperCAmelCase							:			str     =						tokenizer.convert_ids_to_tokens(__lowercase      )
        self.assertListEqual(
            __lowercase	,   ["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]	,   )
      def 			UpperCAmelCase   (   self      :    Optional[Any]      )							->   Optional[int]:
        __UpperCAmelCase							:			Tuple     =						ErnieMTokenizer(__lowercase	,   unk_token="""<unk>"""	,   pad_token="""<pad>"""      )

        __UpperCAmelCase							:			Optional[int]     =						tokenizer.encode("""sequence builders"""      )
        __UpperCAmelCase							:			Optional[Any]     =						tokenizer.encode("""multi-sequence build"""      )

        __UpperCAmelCase							:			List[Any]     =						tokenizer.build_inputs_with_special_tokens(__lowercase      )
        __UpperCAmelCase							:			str     =						tokenizer.build_inputs_with_special_tokens(__lowercase	,   __lowercase      )

        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 			UpperCAmelCase   (   self      :    Optional[int]      )							->   Optional[Any]:
        # fmt: off
        __UpperCAmelCase							:			str     =						{"""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=__lowercase	,   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.""",
            ]	,   )










import decimal






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			str     =						0
  __UpperCAmelCase							:			Tuple     =						decimal.Context(prec=105	)
  for i in range(2						,	100	):
    __UpperCAmelCase							:			Optional[int]     =						decimal.Decimal(__lowerCamelCase	)
    __UpperCAmelCase							:			Any     =						number.sqrt(__lowerCamelCase	)
    if len(str(__lowerCamelCase	)	) > 1:
      answer += int(str(__lowerCamelCase	)[0]	)
      __UpperCAmelCase							:			Optional[Any]     =						str(__lowerCamelCase	)[2:101]
      answer += sum(int(__lowerCamelCase	) for x in sqrt_number_str	)
  return answer


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(f"""{solution() = }""")


