





import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Any		     =      LEDTokenizer
     __snake_case   :     Optional[int]		     =      LEDTokenizerFast
     __snake_case   :     List[Any]		     =      True






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Any:




           '''simple docstring'''


           super().setUp()
           SCREAMING_SNAKE_CASE		=						[
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """\u0120""",
               """\u0120l""",
               """\u0120n""",
               """\u0120lo""",
               """\u0120low""",
               """er""",
               """\u0120lowest""",
               """\u0120newer""",
               """\u0120wider""",
               """<unk>""",
           ]
           SCREAMING_SNAKE_CASE		=						dict(zip(lowerCamelCase__						,range(len(lowerCamelCase__	)	)	)	)
           SCREAMING_SNAKE_CASE		=						["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           SCREAMING_SNAKE_CASE		=						{"""unk_token""": """<unk>"""}

           SCREAMING_SNAKE_CASE		=						os.path.join(self.tmpdirname						,VOCAB_FILES_NAMES["""vocab_file"""]	)
           SCREAMING_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     SCREAMING_SNAKE_CASE__  (							self						:   Any						,**lowerCamelCase__						:   List[str]	)					->		Dict:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]						,**lowerCamelCase__						:   Optional[int]	)					->		List[Any]:




           '''simple docstring'''


           kwargs.update(self.special_tokens_map	)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,**lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   List[Any]	)					->		int:




           '''simple docstring'''


           return "lower newer", "lower newer"






     @cached_property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Any:




           '''simple docstring'''


           return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""	)






     @cached_property
     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Union[str, Any]:




           '''simple docstring'''


           return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""	)






     @require_torch
     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           SCREAMING_SNAKE_CASE		=						[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 SCREAMING_SNAKE_CASE		=						tokenizer(lowerCamelCase__						,max_length=len(lowerCamelCase__	)						,padding=lowerCamelCase__						,return_tensors="""pt"""	)
                 self.assertIsInstance(lowerCamelCase__						,lowerCamelCase__	)

                 self.assertEqual((2, 9)						,batch.input_ids.shape	)
                 self.assertEqual((2, 9)						,batch.attention_mask.shape	)
                 SCREAMING_SNAKE_CASE		=						batch.input_ids.tolist()[0]
                 self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)






     @require_torch
     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 SCREAMING_SNAKE_CASE		=						tokenizer(lowerCamelCase__						,padding=lowerCamelCase__						,return_tensors="""pt"""	)
                 self.assertIn("""input_ids"""						,lowerCamelCase__	)
                 self.assertIn("""attention_mask"""						,lowerCamelCase__	)
                 self.assertNotIn("""labels"""						,lowerCamelCase__	)
                 self.assertNotIn("""decoder_attention_mask"""						,lowerCamelCase__	)






     @require_torch
     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[
               """Summary of the text.""",
               """Another summary.""",
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 SCREAMING_SNAKE_CASE		=						tokenizer(text_target=lowerCamelCase__						,max_length=32						,padding="""max_length"""						,return_tensors="""pt"""	)
                 self.assertEqual(32						,targets["""input_ids"""].shape[1]	)






     @require_torch
     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		List[Any]:




           '''simple docstring'''


           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 SCREAMING_SNAKE_CASE		=						tokenizer(
                     ["""I am a small frog""" * 1024, """I am a small frog"""]						,padding=lowerCamelCase__						,truncation=lowerCamelCase__						,return_tensors="""pt"""	)
                 self.assertIsInstance(lowerCamelCase__						,lowerCamelCase__	)
                 self.assertEqual(batch.input_ids.shape						,(2, 5122)	)






     @require_torch
     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						["""A long paragraph for summarization."""]
           SCREAMING_SNAKE_CASE		=						[
               """Summary of the text.""",
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 SCREAMING_SNAKE_CASE		=						tokenizer(lowerCamelCase__						,return_tensors="""pt"""	)
                 SCREAMING_SNAKE_CASE		=						tokenizer(text_target=lowerCamelCase__						,return_tensors="""pt"""	)
                 SCREAMING_SNAKE_CASE		=						inputs["""input_ids"""]
                 SCREAMING_SNAKE_CASE		=						targets["""input_ids"""]
                 self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
                 self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
                 self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
                 self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)






     @require_torch
     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Optional[int]:




           '''simple docstring'''


           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 SCREAMING_SNAKE_CASE		=						["""Summary of the text.""", """Another summary."""]
                 SCREAMING_SNAKE_CASE		=						[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

                 SCREAMING_SNAKE_CASE		=						tokenizer(lowerCamelCase__						,padding=lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE		=						[[0] * len(lowerCamelCase__	) for x in encoded_output["""input_ids"""]]
                 SCREAMING_SNAKE_CASE		=						tokenizer.pad(lowerCamelCase__	)
                 self.assertSequenceEqual(outputs["""global_attention_mask"""]						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		int:




           '''simple docstring'''


           pass






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Tuple:




           '''simple docstring'''


           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
                       SCREAMING_SNAKE_CASE		=						self.rust_tokenizer_class.from_pretrained(lowerCamelCase__						,**lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						self.tokenizer_class.from_pretrained(lowerCamelCase__						,**lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						"""A, <mask> AllenNLP sentence."""
                       SCREAMING_SNAKE_CASE		=						tokenizer_r.encode_plus(lowerCamelCase__						,add_special_tokens=lowerCamelCase__						,return_token_type_ids=lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						tokenizer_p.encode_plus(lowerCamelCase__						,add_special_tokens=lowerCamelCase__						,return_token_type_ids=lowerCamelCase__	)
                       self.assertEqual(sum(tokens_r["""token_type_ids"""]	)						,sum(tokens_p["""token_type_ids"""]	)	)
                       self.assertEqual(
                           sum(tokens_r["""attention_mask"""]	) / len(tokens_r["""attention_mask"""]	)						,sum(tokens_p["""attention_mask"""]	) / len(tokens_p["""attention_mask"""]	)						,)

                       SCREAMING_SNAKE_CASE		=						tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]	)
                       SCREAMING_SNAKE_CASE		=						tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]	)
                       self.assertSequenceEqual(tokens_p["""input_ids"""]						,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)
                       self.assertSequenceEqual(tokens_r["""input_ids"""]						,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)

                       self.assertSequenceEqual(
                           lowerCamelCase__						,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)
                       self.assertSequenceEqual(
                           lowerCamelCase__						,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)









import json
import sys



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Any:


      '''simple docstring'''


      with open(_SCREAMING_SNAKE_CASE			,						encoding="""utf-8"""     ) as f:
            SCREAMING_SNAKE_CASE		=						json.load(_SCREAMING_SNAKE_CASE     )

      SCREAMING_SNAKE_CASE		=						["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]

      for benchmark_name in sorted(_SCREAMING_SNAKE_CASE     ):
            SCREAMING_SNAKE_CASE		=						results[benchmark_name]

            SCREAMING_SNAKE_CASE		=						benchmark_name.split("""/"""     )[-1]
            output_md.append(F"""### Benchmark: {benchmark_file_name}"""     )

            SCREAMING_SNAKE_CASE		=						"""| metric |"""
            SCREAMING_SNAKE_CASE		=						"""|--------|"""
            SCREAMING_SNAKE_CASE		=						"""| new / old (diff) |"""
            for metric_name in sorted(_SCREAMING_SNAKE_CASE     ):
                  SCREAMING_SNAKE_CASE		=						benchmark_res[metric_name]
                  SCREAMING_SNAKE_CASE		=						metric_vals["""new"""]
                  SCREAMING_SNAKE_CASE		=						metric_vals.get("""old"""			,						_SCREAMING_SNAKE_CASE     )
                  SCREAMING_SNAKE_CASE		=						metric_vals.get("""diff"""			,						_SCREAMING_SNAKE_CASE     )

                  SCREAMING_SNAKE_CASE		=						F""" {new_val:f}""" if isinstance(_SCREAMING_SNAKE_CASE			,						(int, float)     ) else """None"""

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

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

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

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

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


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

      format_json_to_md(input_json_file, output_md_file)


