

'''simple docstring'''





import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class __A    (						__snake_case     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Union[str, Any]    = LongformerTokenizer
 __lowerCamelCase					: Optional[Any]    = True
 __lowerCamelCase					: Any    = LongformerTokenizerFast
 __lowerCamelCase					: List[str]    = True



 def       a__						(self )					->  int:


      """simple docstring"""



      super().setUp()

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

      _a											=						os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES['''vocab_file'''] )
      _a											=						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       a__						(self				,     **A )					->  Union[str, Any]:


      """simple docstring"""



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



 def       a__						(self				,     **A )					->  Optional[Any]:


      """simple docstring"""



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



 def       a__						(self				,     A )					->  Optional[Any]:


      """simple docstring"""



      _a											=						"lower newer"
      _a											=						"lower newer"
      return input_text, output_text



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


      """simple docstring"""



      _a											=						self.tokenizer_class(self.vocab_file				,     self.merges_file				,     **self.special_tokens_map )
      _a											=						"lower newer"
      _a											=						["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
      _a											=						tokenizer.tokenize(A_ )  # , add_prefix_space=True)
      self.assertListEqual(A_				,     A_ )

      _a											=						tokens + [tokenizer.unk_token]
      _a											=						[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ )				,     A_ )



 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						self.get_tokenizer()

      self.assertListEqual(tokenizer.encode('''Hello world!'''				,     add_special_tokens=A_ )				,     [0, 31_414, 232, 328, 2] )
      self.assertListEqual(
          tokenizer.encode('''Hello world! cécé herlolip 418'''				,     add_special_tokens=A_ )				,     [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]				,     )



 @slow
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096''' )

      _a											=						tokenizer.encode('''sequence builders'''				,     add_special_tokens=A_ )
      _a											=						tokenizer.encode('''multi-sequence build'''				,     add_special_tokens=A_ )

      _a											=						tokenizer.encode(
          '''sequence builders'''				,     add_special_tokens=A_				,     add_prefix_space=A_ )
      _a											=						tokenizer.encode(
          '''sequence builders'''				,     '''multi-sequence build'''				,     add_special_tokens=A_				,     add_prefix_space=A_ )

      _a											=						tokenizer.build_inputs_with_special_tokens(A_ )
      _a											=						tokenizer.build_inputs_with_special_tokens(A_				,     A_ )

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						self.get_tokenizer()

      _a											=						"Encode this sequence."
      _a											=						tokenizer.byte_encoder[" ".encode('''utf-8''' )[0]]

      # Testing encoder arguments
      _a											=						tokenizer.encode(A_				,     add_special_tokens=A_				,     add_prefix_space=A_ )
      _a											=						tokenizer.convert_ids_to_tokens(encoded[0] )[0]
      self.assertNotEqual(A_				,     A_ )

      _a											=						tokenizer.encode(A_				,     add_special_tokens=A_				,     add_prefix_space=A_ )
      _a											=						tokenizer.convert_ids_to_tokens(encoded[0] )[0]
      self.assertEqual(A_				,     A_ )

      tokenizer.add_special_tokens({'''bos_token''': '''<s>'''} )
      _a											=						tokenizer.encode(A_				,     add_special_tokens=A_ )
      _a											=						tokenizer.convert_ids_to_tokens(encoded[1] )[0]
      self.assertNotEqual(A_				,     A_ )

      # Testing spaces after special tokens
      _a											=						"<mask>"
      tokenizer.add_special_tokens(
          {'''mask_token''': AddedToken(A_				,     lstrip=A_				,     rstrip=A_ )} )  # mask token has a left space
      _a											=						tokenizer.convert_tokens_to_ids(A_ )

      _a											=						"Encode <mask> sequence"
      _a											=						"Encode <mask>sequence"

      _a											=						tokenizer.encode(A_ )
      _a											=						encoded.index(A_ )
      _a											=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
      self.assertEqual(A_				,     A_ )

      _a											=						tokenizer.encode(A_ )
      _a											=						encoded.index(A_ )
      _a											=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
      self.assertNotEqual(A_				,     A_ )



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      pass



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



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

                _a											=						tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
                _a											=						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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
                self.assertSequenceEqual(tokens_r['''input_ids''']				,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )

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



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      for trim_offsets, add_prefix_space in itertools.product([True, False]				,     repeat=2 ):
           _a											=						self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_ )

           _a											=						json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
           _a											=						json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

           self.assertEqual(pre_tokenizer_state['''add_prefix_space''']				,     A_ )

           self.assertEqual(post_processor_state['''add_prefix_space''']				,     A_ )
           self.assertEqual(post_processor_state['''trim_offsets''']				,     A_ )



 def       a__						(self )					->  Dict:


      """simple docstring"""



      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
                _a											=						"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                _a											=						f'''{text_of_1_token} {text_of_1_token}'''

                _a											=						self.rust_tokenizer_class.from_pretrained(
                    A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_ )
                _a											=						tokenizer_r(A_				,     return_offsets_mapping=A_				,     add_special_tokens=A_ )
                self.assertEqual(encoding.offset_mapping[0]				,     (0, len(A_ )) )
                self.assertEqual(
                    encoding.offset_mapping[1]				,     (len(A_ ) + 1, len(A_ ) + 1 + len(A_ ))				,     )

                _a											=						self.rust_tokenizer_class.from_pretrained(
                    A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_ )
                _a											=						tokenizer_r(A_				,     return_offsets_mapping=A_				,     add_special_tokens=A_ )
                self.assertEqual(encoding.offset_mapping[0]				,     (0, len(A_ )) )
                self.assertEqual(
                    encoding.offset_mapping[1]				,     (len(A_ ) + 1, len(A_ ) + 1 + len(A_ ))				,     )

                _a											=						self.rust_tokenizer_class.from_pretrained(
                    A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_ )
                _a											=						tokenizer_r(A_				,     return_offsets_mapping=A_				,     add_special_tokens=A_ )
                self.assertEqual(encoding.offset_mapping[0]				,     (0, len(A_ )) )
                self.assertEqual(
                    encoding.offset_mapping[1]				,     (len(A_ ), len(A_ ) + 1 + len(A_ ))				,     )

                _a											=						self.rust_tokenizer_class.from_pretrained(
                    A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_ )
                _a											=						tokenizer_r(A_				,     return_offsets_mapping=A_				,     add_special_tokens=A_ )
                self.assertEqual(encoding.offset_mapping[0]				,     (0, len(A_ )) )
                self.assertEqual(
                    encoding.offset_mapping[1]				,     (len(A_ ), len(A_ ) + 1 + len(A_ ))				,     )

                _a											=						f''' {text}'''

                # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                # )
                # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                # self.assertEqual(
                #     encoding.offset_mapping[1],
                #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                # )

                _a											=						self.rust_tokenizer_class.from_pretrained(
                    A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_ )
                _a											=						tokenizer_r(A_				,     return_offsets_mapping=A_				,     add_special_tokens=A_ )
                self.assertEqual(encoding.offset_mapping[0]				,     (1, 1 + len(A_ )) )
                self.assertEqual(
                    encoding.offset_mapping[1]				,     (1 + len(A_ ) + 1, 1 + len(A_ ) + 1 + len(A_ ))				,     )

                _a											=						self.rust_tokenizer_class.from_pretrained(
                    A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_ )
                _a											=						tokenizer_r(A_				,     return_offsets_mapping=A_				,     add_special_tokens=A_ )
                self.assertEqual(encoding.offset_mapping[0]				,     (0, 1 + len(A_ )) )
                self.assertEqual(
                    encoding.offset_mapping[1]				,     (1 + len(A_ ), 1 + len(A_ ) + 1 + len(A_ ))				,     )

                _a											=						self.rust_tokenizer_class.from_pretrained(
                    A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_ )
                _a											=						tokenizer_r(A_				,     return_offsets_mapping=A_				,     add_special_tokens=A_ )
                self.assertEqual(encoding.offset_mapping[0]				,     (0, 1 + len(A_ )) )
                self.assertEqual(
                    encoding.offset_mapping[1]				,     (1 + len(A_ ), 1 + len(A_ ) + 1 + len(A_ ))				,     )








'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch

  from transformers.generation import DisjunctiveConstraint






@require_torch
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						[[1, 2, 4], [1, 2, 3, 4]]
      _a											=						DisjunctiveConstraint(A )
      self.assertTrue(isinstance(dc.token_ids				,     A ) )

      with self.assertRaises(A ):
           DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )

      with self.assertRaises(A ):
           DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						[[1, 2], [1, 2, 3, 4]]

      with self.assertRaises(A ):
           DisjunctiveConstraint(A )  # fails here



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4]]

      _a											=						DisjunctiveConstraint(A )

      _a						,					_a						,					_a											=						dc.update(1 )
      _a											=						stepped is True and completed is False and reset is False
      self.assertTrue(A )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      _a											=						stepped is True and completed is False and reset is False
      self.assertTrue(A )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(3 )
      _a											=						stepped is True and completed is True and reset is False
      self.assertTrue(A )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 3] )



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

      _a											=						DisjunctiveConstraint(A )

      _a						,					_a						,					_a											=						dc.update(1 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(4 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2, 4] )

      _a						,					_a						,					_a											=						dc.update(5 )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 4, 5] )

      dc.reset()

      _a						,					_a						,					_a											=						dc.update(1 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.remaining() == 3 )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.remaining() == 2 )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(5 )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.remaining() == 0 )
      self.assertTrue(dc.current_seq == [1, 2, 5] )



