

"""simple docstring"""






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors
@require_tokenizers
class        _lowercase					(     __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       MvpTokenizer
     _lowerCamelCase     =       MvpTokenizerFast
     _lowerCamelCase     =       True
     _lowerCamelCase     =       filter_roberta_detectors







     def 	lowerCAmelCase__    (      self       ):
      super().setUp()
      __magic_name__     =      [
          '''l''',
          '''o''',
          '''w''',
          '''e''',
          '''r''',
          '''s''',
          '''t''',
          '''i''',
          '''d''',
          '''n''',
          '''\u0120''',
          '''\u0120l''',
          '''\u0120n''',
          '''\u0120lo''',
          '''\u0120low''',
          '''er''',
          '''\u0120lowest''',
          '''\u0120newer''',
          '''\u0120wider''',
          '''<unk>''',
      ]
      __magic_name__     =      dict(zip(__lowerCAmelCase ,						range(len(__lowerCAmelCase       )       )       )       )
      __magic_name__     =      ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
      __magic_name__     =      {'''unk_token''': '''<unk>'''}

      __magic_name__     =      os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']       )
      __magic_name__     =      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 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      kwargs.update(self.special_tokens_map       )
      return self.tokenizer_class.from_pretrained(self.tmpdirname ,						**__lowerCAmelCase       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      kwargs.update(self.special_tokens_map       )
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,						**__lowerCAmelCase       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      return "lower newer", "lower newer"







     @cached_property
     def 	lowerCAmelCase__    (      self       ):
      return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''       )







     @cached_property
     def 	lowerCAmelCase__    (      self       ):
      return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''       )







     @require_torch
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      __magic_name__     =      [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __magic_name__     =      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       )
       __magic_name__     =      batch.input_ids.tolist()[0]
       self.assertListEqual(__lowerCAmelCase ,						__lowerCAmelCase       )
       # Test that special tokens are reset







     @require_torch
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __magic_name__     =      tokenizer(__lowerCAmelCase ,						padding=__lowerCAmelCase ,						return_tensors='''pt'''       )
       # check if input_ids are returned and no labels
       self.assertIn('''input_ids''' ,						__lowerCAmelCase       )
       self.assertIn('''attention_mask''' ,						__lowerCAmelCase       )
       self.assertNotIn('''labels''' ,						__lowerCAmelCase       )
       self.assertNotIn('''decoder_attention_mask''' ,						__lowerCAmelCase       )







     @require_torch
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      [
          '''Summary of the text.''',
          '''Another summary.''',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __magic_name__     =      tokenizer(text_target=__lowerCAmelCase ,						max_length=32 ,						padding='''max_length''' ,						return_tensors='''pt'''       )
       self.assertEqual(32 ,						targets['''input_ids'''].shape[1]       )







     @require_torch
     def 	lowerCAmelCase__    (      self       ):







      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __magic_name__     =      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, 1024)       )







     @require_torch
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      ['''A long paragraph for summarization.''']
      __magic_name__     =      [
          '''Summary of the text.''',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __magic_name__     =      tokenizer(__lowerCAmelCase ,						text_target=__lowerCAmelCase ,						return_tensors='''pt'''       )
       __magic_name__     =      inputs['''input_ids''']
       __magic_name__     =      inputs['''labels''']
       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()       )







     def 	lowerCAmelCase__    (      self       ):
      pass







     def 	lowerCAmelCase__    (      self       ):







      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
        __magic_name__     =      self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase ,						**__lowerCAmelCase       )
        __magic_name__     =      self.tokenizer_class.from_pretrained(__lowerCAmelCase ,						**__lowerCAmelCase       )
        __magic_name__     =      '''A, <mask> AllenNLP sentence.'''
        __magic_name__     =      tokenizer_r.encode_plus(__lowerCAmelCase ,						add_special_tokens=__lowerCAmelCase ,						return_token_type_ids=__lowerCAmelCase       )
        __magic_name__     =      tokenizer_p.encode_plus(__lowerCAmelCase ,						add_special_tokens=__lowerCAmelCase ,						return_token_type_ids=__lowerCAmelCase       )

        # token_type_ids should put 0 everywhere
        self.assertEqual(sum(tokens_r['''token_type_ids''']       ) ,						sum(tokens_p['''token_type_ids''']       )       )

        # attention_mask should put 1 everywhere, so sum over length should be 1
        self.assertEqual(
            sum(tokens_r['''attention_mask''']       ) / len(tokens_r['''attention_mask''']       ) ,						sum(tokens_p['''attention_mask''']       ) / len(tokens_p['''attention_mask''']       ) ,						)

        __magic_name__     =      tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']       )
        __magic_name__     =      tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']       )

        # Rust correctly handles the space before the mask while python doesnt
        self.assertSequenceEqual(tokens_p['''input_ids'''] ,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]       )
        self.assertSequenceEqual(tokens_r['''input_ids'''] ,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 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>''']       )




"""simple docstring"""






from pathlib import Path

import fire





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      Optional[int]:
 __magic_name__     =      Path(__UpperCamelCase						)
 __magic_name__     =      Path(__UpperCamelCase						)
 dest_dir.mkdir(exist_ok=__UpperCamelCase						)
 for path in src_dir.iterdir():
  __magic_name__     =      [x.rstrip() for x in list(path.open().readlines()						)][:n]
  __magic_name__     =      dest_dir.joinpath(path.name						)
  print(__UpperCamelCase						)
  dest_path.open('''w'''						).write('''\n'''.join(__UpperCamelCase						)						)


if __name__ == "__main__":
 fire.Fire(minify)

