




'''simple docstring'''






import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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   SCREAMING_SNAKE_CASE_		(       SCREAMING_SNAKE_CASE__		,     unittest.TestCase				):
 __snake_case	:     List[str]			   =      BartTokenizer
 __snake_case	:     List[Any]			   =      BartTokenizerFast
 __snake_case	:     Any			   =      True
 __snake_case	:     Dict			   =      filter_roberta_detectors



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        super().setUp()
        lowercase  :		List[Any]						=      [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        lowercase  :		Optional[int]						=      dict(zip(a_   ,		range(len(a_ ) ) ) )
        lowercase  :		Optional[Any]						=      ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        lowercase  :		Dict						=      {'''unk_token''': '''<unk>'''}

        lowercase  :		Optional[int]						=      os.path.join(self.tmpdirname   ,		VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase  :		Optional[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(a_ ) + '''\n''' )
        with open(self.merges_file   ,		'''w'''   ,		encoding='''utf-8''' ) as fp:
               fp.write('''\n'''.join(a_ ) )



 def    SCREAMING_SNAKE_CASE(  self   ,		**A ):






        '''simple docstring'''




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



 def    SCREAMING_SNAKE_CASE(  self   ,		**A ):






        '''simple docstring'''




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



 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        return "lower newer", "lower newer"



 @cached_property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return BartTokenizer.from_pretrained('''facebook/bart-large''' )



 @cached_property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return BartTokenizerFast.from_pretrained('''facebook/bart-large''' )



 @require_torch
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		int						=      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        lowercase  :		Any						=      [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase  :		Dict						=      tokenizer(a_   ,		max_length=len(a_ )   ,		padding=a_   ,		return_tensors='''pt''' )
               self.assertIsInstance(a_   ,		a_ )

               self.assertEqual((2, 9)   ,		batch.input_ids.shape )
               self.assertEqual((2, 9)   ,		batch.attention_mask.shape )
               lowercase  :		Any						=      batch.input_ids.tolist()[0]
               self.assertListEqual(a_   ,		a_ )
               # Test that special tokens are reset



 @require_torch
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Any						=      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase  :		Union[str, Any]						=      tokenizer(a_   ,		padding=a_   ,		return_tensors='''pt''' )
               # check if input_ids are returned and no labels
               self.assertIn('''input_ids'''   ,		a_ )
               self.assertIn('''attention_mask'''   ,		a_ )
               self.assertNotIn('''labels'''   ,		a_ )
               self.assertNotIn('''decoder_attention_mask'''   ,		a_ )



 @require_torch
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      [
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase  :		List[Any]						=      tokenizer(text_target=a_   ,		max_length=32   ,		padding='''max_length'''   ,		return_tensors='''pt''' )
               self.assertEqual(32   ,		targets['''input_ids'''].shape[1] )



 @require_torch
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase  :		Any						=      tokenizer(
                   ['''I am a small frog''' * 1024, '''I am a small frog''']   ,		padding=a_   ,		truncation=a_   ,		return_tensors='''pt''' )
               self.assertIsInstance(a_   ,		a_ )
               self.assertEqual(batch.input_ids.shape   ,		(2, 1024) )



 @require_torch
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      ['''A long paragraph for summarization.''']
        lowercase  :		str						=      [
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase  :		str						=      tokenizer(a_   ,		return_tensors='''pt''' )
               lowercase  :		Optional[int]						=      tokenizer(text_target=a_   ,		return_tensors='''pt''' )
               lowercase  :		Any						=      inputs['''input_ids''']
               lowercase  :		List[str]						=      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() )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        pass





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                      lowercase  :		int						=      self.rust_tokenizer_class.from_pretrained(a_   ,		**a_ )
                      lowercase  :		List[Any]						=      self.tokenizer_class.from_pretrained(a_   ,		**a_ )
                      lowercase  :		List[str]						=      '''A, <mask> AllenNLP sentence.'''
                      lowercase  :		List[str]						=      tokenizer_r.encode_plus(a_   ,		add_special_tokens=a_   ,		return_token_type_ids=a_ )
                      lowercase  :		Tuple						=      tokenizer_p.encode_plus(a_   ,		add_special_tokens=a_   ,		return_token_type_ids=a_ )

                      # 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'''] )   ,		)

                      lowercase  :		List[Any]						=      tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
                      lowercase  :		Tuple						=      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, 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(
                          a_   ,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
                      self.assertSequenceEqual(
                          a_   ,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase	:      Optional[int]				=       {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase	:      List[str]				=       [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

      lowercase	:      int				=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




