




"""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  lowercase(					unittest.TestCase			):







 '''simple docstring'''





 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  [[1, 2, 4], [1, 2, 3, 4]]
   _snake_case							:		List[Any]								=  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 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  [[1, 2], [1, 2, 3, 4]]

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


 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  [[1, 2, 3], [1, 2, 4]]

   _snake_case							:		int								=  DisjunctiveConstraint(a_		)

   _snake_case		, _snake_case		, _snake_case							:		List[str]								=  dc.update(1		)
   _snake_case							:		Tuple								=  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]		)

   _snake_case		, _snake_case		, _snake_case							:		Optional[int]								=  dc.update(2		)
   _snake_case							:		Any								=  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]		)

   _snake_case		, _snake_case		, _snake_case							:		Optional[int]								=  dc.update(3		)
   _snake_case							:		Tuple								=  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 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

   _snake_case							:		str								=  DisjunctiveConstraint(a_		)

   _snake_case		, _snake_case		, _snake_case							:		Union[str, Any]								=  dc.update(1		)
   self.assertTrue(not dc.completed		)
   self.assertTrue(dc.current_seq == [1]		)

   _snake_case		, _snake_case		, _snake_case							:		Optional[int]								=  dc.update(2		)
   self.assertTrue(not dc.completed		)
   self.assertTrue(dc.current_seq == [1, 2]		)

   _snake_case		, _snake_case		, _snake_case							:		Union[str, Any]								=  dc.update(4		)
   self.assertTrue(not dc.completed		)
   self.assertTrue(dc.current_seq == [1, 2, 4]		)

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

   dc.reset()

   _snake_case		, _snake_case		, _snake_case							:		Optional[int]								=  dc.update(1		)
   self.assertTrue(not dc.completed		)
   self.assertTrue(dc.remaining() == 3		)
   self.assertTrue(dc.current_seq == [1]		)

   _snake_case		, _snake_case		, _snake_case							:		List[Any]								=  dc.update(2		)
   self.assertTrue(not dc.completed		)
   self.assertTrue(dc.remaining() == 2		)
   self.assertTrue(dc.current_seq == [1, 2]		)

   _snake_case		, _snake_case		, _snake_case							:		Union[str, Any]								=  dc.update(5		)
   self.assertTrue(dc.completed		)  # Completed!
   self.assertTrue(dc.remaining() == 0		)
   self.assertTrue(dc.current_seq == [1, 2, 5]		)








"""simple docstring"""


import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A_      =    get_tests_dir('''fixtures/spiece.model''')






@require_sentencepiece
@require_tokenizers
class  lowercase(					__a	,      unittest.TestCase			):







 '''simple docstring'''





 lowercase__			 = AlbertTokenizer
 lowercase__			 = AlbertTokenizerFast
 lowercase__			 = True
 lowercase__			 = True
 lowercase__			 = True


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   super().setUp()

   # We have a SentencePiece fixture for testing
   _snake_case							:		Optional[int]								=  AlbertTokenizer(a_		)
   tokenizer.save_pretrained(self.tmpdirname		)


 def 			UpperCamelCase_       (    self:		Optional[int],				a_:		int		):




   '''simple docstring'''



   _snake_case							:		Dict								=  """this is a test"""
   _snake_case							:		Optional[int]								=  """this is a test"""
   return input_text, output_text


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   _snake_case							:		str								=  """<pad>"""
   _snake_case							:		List[Any]								=  0

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_		),				a_		)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_		),				a_		)


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  list(self.get_tokenizer().get_vocab().keys()		)

   self.assertEqual(vocab_keys[0],				"""<pad>"""		)
   self.assertEqual(vocab_keys[1],				"""<unk>"""		)
   self.assertEqual(vocab_keys[-1],				"""▁eloquent"""		)
   self.assertEqual(len(a_		),				30_000		)


 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   self.assertEqual(self.get_tokenizer().vocab_size,				30_000		)


 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   if not self.test_rust_tokenizer:
     return

   _snake_case							:		Tuple								=  self.get_tokenizer()
   _snake_case							:		List[str]								=  self.get_rust_tokenizer()

   _snake_case							:		Optional[int]								=  """I was born in 92000, and this is falsé."""

   _snake_case							:		Optional[Any]								=  tokenizer.tokenize(a_		)
   _snake_case							:		List[str]								=  rust_tokenizer.tokenize(a_		)
   self.assertListEqual(a_,				a_		)

   _snake_case							:		Any								=  tokenizer.encode(a_,				add_special_tokens=a_		)
   _snake_case							:		Any								=  rust_tokenizer.encode(a_,				add_special_tokens=a_		)
   self.assertListEqual(a_,				a_		)

   _snake_case							:		int								=  self.get_rust_tokenizer()
   _snake_case							:		Dict								=  tokenizer.encode(a_		)
   _snake_case							:		Optional[int]								=  rust_tokenizer.encode(a_		)
   self.assertListEqual(a_,				a_		)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  AlbertTokenizer(a_,				keep_accents=a_		)

   _snake_case							:		Any								=  tokenizer.tokenize("""This is a test"""		)
   self.assertListEqual(a_,				["""▁this""", """▁is""", """▁a""", """▁test"""]		)

   self.assertListEqual(tokenizer.convert_tokens_to_ids(a_		),				[48, 25, 21, 1_289]		)

   _snake_case							:		Dict								=  tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
   self.assertListEqual(
       a_,				["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]		)
   _snake_case							:		str								=  tokenizer.convert_tokens_to_ids(a_		)
   self.assertListEqual(a_,				[31, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]		)

   _snake_case							:		Tuple								=  tokenizer.convert_ids_to_tokens(a_		)
   self.assertListEqual(
       a_,				["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""],				)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  AlbertTokenizer(a_		)

   _snake_case							:		int								=  tokenizer.encode("""sequence builders"""		)
   _snake_case							:		Optional[int]								=  tokenizer.encode("""multi-sequence build"""		)

   _snake_case							:		Any								=  tokenizer.build_inputs_with_special_tokens(a_		)
   _snake_case							:		Union[str, Any]								=  tokenizer.build_inputs_with_special_tokens(a_,				a_		)

   assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
   assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
       tokenizer.sep_token_id
   ]


 @slow
 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Any								=  {"""attention_mask""": [[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, 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, 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, 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], [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, 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]], """input_ids""": [[2, 21_970, 13, 5, 6_092, 167, 28, 7_103, 2_153, 673, 8, 7_028, 12_051, 18, 17, 7_103, 2_153, 673, 8, 3_515, 18_684, 8, 4_461, 6, 1_927, 297, 8, 12_060, 2_607, 18, 13, 5, 4_461, 15, 10_538, 38, 8, 135, 15, 822, 58, 15, 993, 10_363, 15, 1_460, 8_005, 4_461, 15, 993, 255, 2_328, 9, 9, 9, 6, 26, 1_112, 816, 3_260, 13, 5, 103, 2_377, 6, 17, 1_112, 816, 2_782, 13, 5, 103, 10_641, 6, 29, 84, 2_512, 2_430, 782, 18_684, 2_761, 19, 808, 2_430, 2_556, 17, 855, 1_480, 9_477, 4_091, 128, 11_712, 15, 7_103, 2_153, 673, 17, 24_883, 9_990, 9, 3], [2, 11_502, 25, 1_006, 20, 782, 8, 11_809, 855, 1_732, 19_393, 18_667, 37, 367, 21_018, 69, 1_854, 34, 11_860, 19_124, 27, 156, 225, 17, 193, 4_141, 19, 65, 9_124, 9, 3, 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], [2, 14, 2_231, 886, 2_385, 17_659, 84, 14, 16_792, 1_952, 9, 3, 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]], """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, 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, 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]]}  # noqa: E501
   # fmt: on

   self.tokenizer_integration_test_util(
       expected_encoding=a_,				model_name="""albert-base-v2""",				revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e""",				)


