"""simple docstring"""






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class  _A						(       lowerCAmelCase  ,    unittest.TestCase	):
      snake_case__			:   Dict							  =	DebertaTokenizer
      snake_case__			:   Optional[int]							  =	True
      snake_case__			:   Dict							  =	DebertaTokenizerFast

      def 				A__       (						self						):







            """simple docstring"""



            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            lowercase														=	[
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """[UNK]""",
            ]
            lowercase														=	dict(zip(__lowerCAmelCase		,       range(len(__lowerCAmelCase						)						)						)						)
            lowercase														=	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            lowercase														=	{"""unk_token""": """[UNK]"""}

            lowercase														=	os.path.join(self.tmpdirname		,       VOCAB_FILES_NAMES["""vocab_file"""]						)
            lowercase														=	os.path.join(self.tmpdirname		,       VOCAB_FILES_NAMES["""merges_file"""]						)
            with open(self.vocab_file		,       """w"""		,       encoding="""utf-8"""						) as fp:
                  fp.write(json.dumps(__lowerCAmelCase						) + """\n"""						)
            with open(self.merges_file		,       """w"""		,       encoding="""utf-8"""						) as fp:
                  fp.write("""\n""".join(__lowerCAmelCase						)						)

      def 				A__       (						self		,       **__lowerCAmelCase						):







            """simple docstring"""



            kwargs.update(self.special_tokens_map						)
            return self.tokenizer_class.from_pretrained(self.tmpdirname		,       **__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	"""lower newer"""
            lowercase														=	"""lower newer"""
            return input_text, output_text

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.get_tokenizer()
            lowercase														=	"""lower newer"""
            lowercase														=	["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            lowercase														=	tokenizer.tokenize(__lowerCAmelCase						)
            self.assertListEqual(__lowerCAmelCase		,       __lowerCAmelCase						)

            lowercase														=	tokens + [tokenizer.unk_token]
            lowercase														=	[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase						)		,       __lowerCAmelCase						)

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.get_tokenizer()
            lowercase														=	tokenizer("""Hello"""		,       """World"""						)
            lowercase														=	[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
            self.assertListEqual(tokd["""token_type_ids"""]		,       __lowerCAmelCase						)

      @slow
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""						)

            lowercase														=	tokenizer.encode("""sequence builders"""		,       add_special_tokens=__lowerCAmelCase						)
            lowercase														=	tokenizer.encode("""multi-sequence build"""		,       add_special_tokens=__lowerCAmelCase						)

            lowercase														=	tokenizer.encode(
                """sequence builders"""		,       add_special_tokens=__lowerCAmelCase		,       add_prefix_space=__lowerCAmelCase						)
            lowercase														=	tokenizer.encode(
                """sequence builders"""		,       """multi-sequence build"""		,       add_special_tokens=__lowerCAmelCase		,       add_prefix_space=__lowerCAmelCase						)

            lowercase														=	tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase						)
            lowercase														=	tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase		,       __lowerCAmelCase						)

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode







      @slow
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	[self.tokenizer_class]
            if self.test_rust_tokenizer:
                  tokenizer_classes.append(self.rust_tokenizer_class						)

            for tokenizer_class in tokenizer_classes:
                  lowercase														=	tokenizer_class.from_pretrained("""microsoft/deberta-base"""						)

                  lowercase														=	[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  lowercase														=	tokenizer(__lowerCAmelCase		,       padding=__lowerCAmelCase						)
                  lowercase														=	[tokenizer.decode(__lowerCAmelCase		,       skip_special_tokens=__lowerCAmelCase						) for seq in encoding["""input_ids"""]]

                  # fmt: off
                  lowercase														=	{
                      """input_ids""": [
                          [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 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, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
                      ],
                      """token_type_ids""": [
                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 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

                  lowercase														=	[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  self.assertDictEqual(encoding.data		,       __lowerCAmelCase						)

                  for expected, decoded in zip(__lowerCAmelCase		,       __lowerCAmelCase						):
                        self.assertEqual(__lowerCAmelCase		,       __lowerCAmelCase						)




"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable

__lowerCAmelCase    :       str					     =list[list[float | int]]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Matrix					,		lowerCAmelCase__     :Matrix		) ->    Matrix:
      '''simple docstring'''


      lowercase														=	len(lowerCAmelCase__		)
      lowercase														=	[[0 for _ in range(size + 1		)] for _ in range(lowerCAmelCase__		)]
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42

      for row in range(lowerCAmelCase__		):
            for col in range(lowerCAmelCase__		):
                  lowercase														=	matrix[row][col]

            lowercase														=	vector[row][0]

      lowercase														=	0
      lowercase														=	0
      while row < size and col < size:
            # pivoting
            lowercase														=	max((abs(augmented[rowa][col]		), rowa) for rowa in range(lowerCAmelCase__					,		lowerCAmelCase__		)		)[
                1
            ]
            if augmented[pivot_row][col] == 0:
                  col += 1
                  continue
            else:
                  lowercase							,							lowercase														=	augmented[pivot_row], augmented[row]

            for rowa in range(row + 1					,		lowerCAmelCase__		):
                  lowercase														=	augmented[rowa][col] / augmented[row][col]
                  lowercase														=	0
                  for cola in range(col + 1					,		size + 1		):
                        augmented[rowa][cola] -= augmented[row][cola] * ratio

            row += 1
            col += 1

      # back substitution
      for col in range(1					,		lowerCAmelCase__		):
            for row in range(lowerCAmelCase__		):
                  lowercase														=	augmented[row][col] / augmented[col][col]
                  for cola in range(lowerCAmelCase__					,		size + 1		):
                        augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
      return [
          [round(augmented[row][size] / augmented[row][row]					,		1_0		)] for row in range(lowerCAmelCase__		)
      ]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[int]		) ->    Callable[[int], int]:
      '''simple docstring'''


      lowercase														=	len(lowerCAmelCase__		)
      lowercase														=	[[0 for _ in range(lowerCAmelCase__		)] for _ in range(lowerCAmelCase__		)]
      lowercase														=	[[0] for _ in range(lowerCAmelCase__		)]
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42

      for x_val, y_val in enumerate(lowerCAmelCase__		):
            for col in range(lowerCAmelCase__		):
                  lowercase														=	(x_val + 1) ** (size - col - 1)
            lowercase														=	y_val

      lowercase														=	solve(lowerCAmelCase__					,		lowerCAmelCase__		)

      def interpolated_func(lowerCAmelCase__     :int		) -> int:
            return sum(
                round(coeffs[x_val][0]		) * (var ** (size - x_val - 1))
                for x_val in range(lowerCAmelCase__		)		)

      return interpolated_func


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int		) ->    int:
      '''simple docstring'''


      return (
          1
          - variable
          + variable**2
          - variable**3
          + variable**4
          - variable**5
          + variable**6
          - variable**7
          + variable**8
          - variable**9
          + variable**1_0
      )


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Callable[[int], int] = question_function					,		lowerCAmelCase__     :int = 1_0		) ->    int:
      '''simple docstring'''


      lowercase														=	[func(lowerCAmelCase__		) for x_val in range(1					,		order + 1		)]

      lowercase														=	[
          interpolate(data_points[:max_coeff]		) for max_coeff in range(1					,		order + 1		)
      ]

      lowercase														=	0
      lowercase														=	42
      lowercase														=	42

      for poly in polynomials:
            lowercase														=	1
            while func(lowerCAmelCase__		) == poly(lowerCAmelCase__		):
                  x_val += 1

            ret += poly(lowerCAmelCase__		)

      return ret


if __name__ == "__main__":
       print(F"""{solution() = }""")



