
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        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       unittest.TestCase			):
      _A    :		List[str]     						=  DebertaTokenizer
      _A    :		List[str]     						=  True
      _A    :		Tuple     						=  DebertaTokenizerFast
      def A_      (      self			):
          super().setUp()

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

          snake_case__    =					os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["vocab_file"]			)
          snake_case__    =					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			):
          kwargs.update(self.special_tokens_map			)
          return self.tokenizer_class.from_pretrained(self.tmpdirname		,						**lowerCamelCase			)
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					"lower newer"
          snake_case__    =					"lower newer"
          return input_text, output_text
      def A_      (      self			):
          snake_case__    =					self.get_tokenizer()
          snake_case__    =					"lower newer"
          snake_case__    =					["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
          snake_case__    =					tokenizer.tokenize(lowerCamelCase			)
          self.assertListEqual(lowerCamelCase		,						lowerCamelCase			)

          snake_case__    =					tokens + [tokenizer.unk_token]
          snake_case__    =					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase			)		,						lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.get_tokenizer()
          snake_case__    =					tokenizer("Hello"		,						"World"			)
          snake_case__    =					[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
          self.assertListEqual(tokd["token_type_ids"]		,						lowerCamelCase			)
      @slow
      def A_      (      self			):
          snake_case__    =					self.tokenizer_class.from_pretrained("microsoft/deberta-base"			)

          snake_case__    =					tokenizer.encode("sequence builders"		,						add_special_tokens=lowerCamelCase			)
          snake_case__    =					tokenizer.encode("multi-sequence build"		,						add_special_tokens=lowerCamelCase			)

          snake_case__    =					tokenizer.encode(
              "sequence builders"		,						add_special_tokens=lowerCamelCase		,						add_prefix_space=lowerCamelCase			)
          snake_case__    =					tokenizer.encode(
              "sequence builders"		,						"multi-sequence build"		,						add_special_tokens=lowerCamelCase		,						add_prefix_space=lowerCamelCase			)

          snake_case__    =					tokenizer.build_inputs_with_special_tokens(lowerCamelCase			)
          snake_case__    =					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			):
          snake_case__    =					[self.tokenizer_class]
          if self.test_rust_tokenizer:
              tokenizer_classes.append(self.rust_tokenizer_class			)

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

              snake_case__    =					[
                  "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.",
              ]

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

              # fmt: off
              snake_case__    =					{
                  "input_ids": [
                      [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 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

              snake_case__    =					[
                  "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			)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if any(not isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				) or x < 0 for x in sequence				):
        raise TypeError("Sequence must be list of non-negative integers"				)
    for _ in range(len(__lowerCAmelCase				)				):
        for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase     ,		sequence[1:]				)				):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]



