
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__		(	__SCREAMING_SNAKE_CASE  ,   unittest.TestCase):
      _UpperCAmelCase				:    str           =			DebertaTokenizer
      _UpperCAmelCase				:    Tuple           =			True
      _UpperCAmelCase				:    List[str]           =			DebertaTokenizerFast
      def 						UpperCamelCase__							(      self      ):
       super().setUp()

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

       lowerCamelCase		:	List[Any]							=      os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES["""vocab_file"""]      )
       lowerCamelCase		:	Any							=      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(__magic_name__      ) + """\n"""      )
       with open(self.merges_file    ,  """w"""    ,  encoding="""utf-8"""      ) as fp:
        fp.write("""\n""".join(__magic_name__      )      )
      def 						UpperCamelCase__							(      self    ,  **__magic_name__      ):
       kwargs.update(self.special_tokens_map      )
       return self.tokenizer_class.from_pretrained(self.tmpdirname    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       lowerCamelCase		:	int							=      """lower newer"""
       lowerCamelCase		:	List[Any]							=      """lower newer"""
       return input_text, output_text
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      self.get_tokenizer()
       lowerCamelCase		:	Union[str, Any]							=      """lower newer"""
       lowerCamelCase		:	Union[str, Any]							=      ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
       lowerCamelCase		:	Tuple							=      tokenizer.tokenize(__magic_name__      )
       self.assertListEqual(__magic_name__    ,  __magic_name__      )

       lowerCamelCase		:	int							=      tokens + [tokenizer.unk_token]
       lowerCamelCase		:	Any							=      [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__      )    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[Any]							=      self.get_tokenizer()
       lowerCamelCase		:	Optional[int]							=      tokenizer("""Hello"""    ,  """World"""      )
       lowerCamelCase		:	Optional[int]							=      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd["""token_type_ids"""]    ,  __magic_name__      )
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""      )

       lowerCamelCase		:	Tuple							=      tokenizer.encode("""sequence builders"""    ,  add_special_tokens=__magic_name__      )
       lowerCamelCase		:	int							=      tokenizer.encode("""multi-sequence build"""    ,  add_special_tokens=__magic_name__      )

       lowerCamelCase		:	Optional[Any]							=      tokenizer.encode(
           """sequence builders"""    ,  add_special_tokens=__magic_name__    ,  add_prefix_space=__magic_name__      )
       lowerCamelCase		:	Union[str, Any]							=      tokenizer.encode(
           """sequence builders"""    ,  """multi-sequence build"""    ,  add_special_tokens=__magic_name__    ,  add_prefix_space=__magic_name__      )

       lowerCamelCase		:	str							=      tokenizer.build_inputs_with_special_tokens(__magic_name__      )
       lowerCamelCase		:	Optional[Any]							=      tokenizer.build_inputs_with_special_tokens(__magic_name__    ,  __magic_name__      )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode






      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      [self.tokenizer_class]
       if self.test_rust_tokenizer:
        tokenizer_classes.append(self.rust_tokenizer_class      )

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

        lowerCamelCase		:	List[Any]							=      [
            """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.""",
        ]

        lowerCamelCase		:	List[Any]							=      tokenizer(__magic_name__    ,  padding=__magic_name__      )
        lowerCamelCase		:	Any							=      [tokenizer.decode(__magic_name__    ,  skip_special_tokens=__magic_name__      ) for seq in encoding["""input_ids"""]]

        # fmt: off
        lowerCamelCase		:	Union[str, Any]							=      {
            """input_ids""": [
                [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 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, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 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_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 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

        lowerCamelCase		:	Any							=      [
            """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    ,  __magic_name__      )

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






import json
import sys





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 with open(lowerCamelCase,	encoding="""utf-8"""							) as f:
  lowerCamelCase		:	List[Any]							=      json.load(lowerCamelCase							)

 lowerCamelCase		:	Optional[Any]							=      ["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]

 for benchmark_name in sorted(lowerCamelCase							):
  lowerCamelCase		:	List[Any]							=      results[benchmark_name]

  lowerCamelCase		:	Union[str, Any]							=      benchmark_name.split("""/"""							)[-1]
  output_md.append(F'''### Benchmark: {benchmark_file_name}'''							)

  lowerCamelCase		:	Any							=      """| metric |"""
  lowerCamelCase		:	str							=      """|--------|"""
  lowerCamelCase		:	List[Any]							=      """| new / old (diff) |"""
  for metric_name in sorted(lowerCamelCase							):
   lowerCamelCase		:	List[Any]							=      benchmark_res[metric_name]
   lowerCamelCase		:	Tuple							=      metric_vals["""new"""]
   lowerCamelCase		:	int							=      metric_vals.get("""old""",	lowerCamelCase							)
   lowerCamelCase		:	Dict							=      metric_vals.get("""diff""",	lowerCamelCase							)

   lowerCamelCase		:	Dict							=      F''' {new_val:f}''' if isinstance(lowerCamelCase,	(int, float)							) else """None"""

   if old_val is not None:
    val_str += F''' / {old_val:f}''' if isinstance(lowerCamelCase,	(int, float)							) else "None"
   if dif_val is not None:
    val_str += F''' ({dif_val:f})''' if isinstance(lowerCamelCase,	(int, float)							) else "None"

   title += " " + metric_name + " |"
   lines += "---|"
   value += val_str + " |"

  output_md += [title, lines, value, " "]

 output_md.append("""</details>"""							)

 with open(lowerCamelCase,	"""w""",	encoding="""utf-8"""							) as f:
  f.writelines("""\n""".join(lowerCamelCase							)							)


if __name__ == "__main__":
      _lowerCamelCase					   =sys.argv[1]
      _lowerCamelCase					   =sys.argv[2]

      format_json_to_md(input_json_file, output_md_file)




