
import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__     :		List[str]        = get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
@require_tokenizers
class    _snake_case (						UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  List[Any]	   =      ReformerTokenizer
 __lowerCAmelCase	:  str	   =      ReformerTokenizerFast
 __lowerCAmelCase	:  List[Any]	   =      True
 __lowerCAmelCase	:  Dict	   =      False
 __lowerCAmelCase	:  Tuple	   =      True


 def   lowercase__  (       self):


   '''simple docstring'''




   super().setUp()

   lowercase__	: Any     		=   ReformerTokenizer(SCREAMING_SNAKE_CASE_       ,	keep_accents=SCREAMING_SNAKE_CASE_)
   tokenizer.save_pretrained(self.tmpdirname)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   """<s>"""
   lowercase__	: Optional[Any]     		=   1

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


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   list(self.get_tokenizer().get_vocab().keys())

   self.assertEqual(vocab_keys[0]       ,	"""<unk>""")
   self.assertEqual(vocab_keys[1]       ,	"""<s>""")
   self.assertEqual(vocab_keys[-1]       ,	"""j""")
   self.assertEqual(len(SCREAMING_SNAKE_CASE_)       ,	10_00)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.assertEqual(self.get_tokenizer().vocab_size       ,	10_00)


 def   lowercase__  (       self):


   '''simple docstring'''




   if not self.test_rust_tokenizer:
     return

   lowercase__	: int     		=   self.get_tokenizer()
   lowercase__	: Any     		=   self.get_rust_tokenizer()

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

   lowercase__	: Tuple     		=   tokenizer.tokenize(SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_)
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   tokenizer.encode(SCREAMING_SNAKE_CASE_       ,	add_special_tokens=SCREAMING_SNAKE_CASE_)
   lowercase__	: Tuple     		=   rust_tokenizer.encode(SCREAMING_SNAKE_CASE_       ,	add_special_tokens=SCREAMING_SNAKE_CASE_)
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

   lowercase__	: Any     		=   self.get_rust_tokenizer()
   lowercase__	: int     		=   tokenizer.encode(SCREAMING_SNAKE_CASE_)
   lowercase__	: str     		=   rust_tokenizer.encode(SCREAMING_SNAKE_CASE_)
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_=15):


   '''simple docstring'''




   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
       lowercase__	: str     		=   self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

       # Simple input
       lowercase__	: List[Any]     		=   """This is a simple input"""
       lowercase__	: int     		=   ["""This is a simple input 1""", """This is a simple input 2"""]
       lowercase__	: str     		=   ("""This is a simple input""", """This is a pair""")
       lowercase__	: Optional[int]     		=   [
           ("""This is a simple input 1""", """This is a simple input 2"""),
           ("""This is a simple pair 1""", """This is a simple pair 2"""),
       ]

       # Simple input tests
       self.assertRaises(SCREAMING_SNAKE_CASE_       ,	tokenizer_r.encode       ,	SCREAMING_SNAKE_CASE_       ,	max_length=SCREAMING_SNAKE_CASE_       ,	padding="""max_length""")

       # Simple input
       self.assertRaises(SCREAMING_SNAKE_CASE_       ,	tokenizer_r.encode_plus       ,	SCREAMING_SNAKE_CASE_       ,	max_length=SCREAMING_SNAKE_CASE_       ,	padding="""max_length""")

       # Simple input
       self.assertRaises(
           SCREAMING_SNAKE_CASE_       ,	tokenizer_r.batch_encode_plus       ,	SCREAMING_SNAKE_CASE_       ,	max_length=SCREAMING_SNAKE_CASE_       ,	padding="""max_length"""       ,	)

       # Pair input
       self.assertRaises(SCREAMING_SNAKE_CASE_       ,	tokenizer_r.encode       ,	SCREAMING_SNAKE_CASE_       ,	max_length=SCREAMING_SNAKE_CASE_       ,	padding="""max_length""")

       # Pair input
       self.assertRaises(SCREAMING_SNAKE_CASE_       ,	tokenizer_r.encode_plus       ,	SCREAMING_SNAKE_CASE_       ,	max_length=SCREAMING_SNAKE_CASE_       ,	padding="""max_length""")

       # Pair input
       self.assertRaises(
           SCREAMING_SNAKE_CASE_       ,	tokenizer_r.batch_encode_plus       ,	SCREAMING_SNAKE_CASE_       ,	max_length=SCREAMING_SNAKE_CASE_       ,	padding="""max_length"""       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   ReformerTokenizer(SCREAMING_SNAKE_CASE_       ,	keep_accents=SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   tokenizer.tokenize("""This is a test""")
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

   self.assertListEqual(
       tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_)       ,	[2_85, 46, 10, 1_70, 3_82]       ,	)

   lowercase__	: str     		=   tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
   self.assertListEqual(
       SCREAMING_SNAKE_CASE_       ,	[
           SPIECE_UNDERLINE + """I""",
           SPIECE_UNDERLINE + """was""",
           SPIECE_UNDERLINE + """b""",
           """or""",
           """n""",
           SPIECE_UNDERLINE + """in""",
           SPIECE_UNDERLINE + """""",
           """9""",
           """2""",
           """0""",
           """0""",
           """0""",
           """,""",
           SPIECE_UNDERLINE + """and""",
           SPIECE_UNDERLINE + """this""",
           SPIECE_UNDERLINE + """is""",
           SPIECE_UNDERLINE + """f""",
           """al""",
           """s""",
           """é""",
           """.""",
       ]       ,	)
   lowercase__	: Union[str, Any]     		=   tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_)
   self.assertListEqual(
       SCREAMING_SNAKE_CASE_       ,	[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]       ,	)

   lowercase__	: Optional[int]     		=   tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_)
   self.assertListEqual(
       SCREAMING_SNAKE_CASE_       ,	[
           SPIECE_UNDERLINE + """I""",
           SPIECE_UNDERLINE + """was""",
           SPIECE_UNDERLINE + """b""",
           """or""",
           """n""",
           SPIECE_UNDERLINE + """in""",
           SPIECE_UNDERLINE + """""",
           """<unk>""",
           """2""",
           """0""",
           """0""",
           """0""",
           """,""",
           SPIECE_UNDERLINE + """and""",
           SPIECE_UNDERLINE + """this""",
           SPIECE_UNDERLINE + """is""",
           SPIECE_UNDERLINE + """f""",
           """al""",
           """s""",
           """<unk>""",
           """.""",
       ]       ,	)


 @cached_property
 def   lowercase__  (       self):


   '''simple docstring'''




   return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment""")


 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   """Hello World!"""
   lowercase__	: str     		=   [1_26, 32, 2_62, 1_52, 38, 72, 2_87]

   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_))


 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   (
       """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
       """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
   )
   lowercase__	: List[Any]     		=   [
       1_08,
       2_65,
       24,
       1_11,
       4,
       2_58,
       1_56,
       35,
       28,
       2_75,
       3,
       2_59,
       2_97,
       2_60,
       84,
       4,
       35,
       1_10,
       44,
       8,
       2_59,
       91,
       2_68,
       21,
       11,
       2_09,
       2_74,
       1_09,
       2_66,
       2_77,
       1_17,
       86,
       93,
       3_15,
       2_58,
       2_78,
       2_58,
       2_77,
       2_58,
       0,
       2_58,
       2_88,
       2_58,
       3_19,
       2_58,
       0,
       2_58,
       0,
       2_58,
       0,
       2_58,
       0,
       2_58,
       2_87,
       2_58,
       3_15,
       2_58,
       2_89,
       2_58,
       2_78,
       99,
       2_69,
       2_66,
       2_62,
       8,
       2_59,
       2_41,
       4,
       2_17,
       2_30,
       2_68,
       2_66,
       55,
       1_68,
       1_06,
       75,
       1_93,
       2_66,
       2_23,
       27,
       49,
       26,
       2_82,
       25,
       2_64,
       2_99,
       19,
       26,
       0,
       2_58,
       2_77,
       1_17,
       86,
       93,
       1_76,
       1_83,
       2_70,
       11,
       2_62,
       42,
       61,
       2_65,
   ]

   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_))


 @require_torch
 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   import torch

   from transformers import ReformerConfig, ReformerModel

   # Build sequence
   lowercase__	: Union[str, Any]     		=   list(self.big_tokenizer.get_vocab().keys())[:10]
   lowercase__	: Optional[Any]     		=   """ """.join(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[int]     		=   self.big_tokenizer.encode_plus(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""")
   lowercase__	: Any     		=   self.big_tokenizer.batch_encode_plus([sequence, sequence]       ,	return_tensors="""pt""")

   lowercase__	: Dict     		=   ReformerConfig()
   # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
   lowercase__	: Tuple     		=   encoded_sequence["""input_ids"""].shape
   lowercase__	: Union[str, Any]     		=   ReformerModel(SCREAMING_SNAKE_CASE_)

   # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
   assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

   with torch.no_grad():
     model(**SCREAMING_SNAKE_CASE_)
     model(**SCREAMING_SNAKE_CASE_)



 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   {"""input_ids""": [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], """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]]}  # noqa: E501
   # fmt: on

   # This tokenizer does not know some characters like ")".
   # That is the reason why we use very simple texts here.
   # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
   lowercase__	: Dict     		=   [
       """This is a very simple sentence.""",
       """The quick brown fox jumps over the lazy dog.""",
   ]

   self.tokenizer_integration_test_util(
       expected_encoding=SCREAMING_SNAKE_CASE_       ,	model_name="""google/reformer-crime-and-punishment"""       ,	revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""       ,	padding=SCREAMING_SNAKE_CASE_       ,	sequences=SCREAMING_SNAKE_CASE_       ,	)








def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						List[str]:


  '''simple docstring'''






  global f  # a global dp table for knapsack
  if f[i][j] < 0:
    if j < wt[i - 1]:
      lowercase__	: str     		=   mf_knapsack(i - 1			,			lowercase_			,			lowercase_			,			lowercase_ )
    else:
      lowercase__	: List[str]     		=   max(
          mf_knapsack(i - 1			,			lowercase_			,			lowercase_			,			lowercase_ )			,			mf_knapsack(i - 1			,			lowercase_			,			lowercase_			,			j - wt[i - 1] ) + val[i - 1]			,			)
    lowercase__	: List[Any]     		=   val
  return f[i][j]

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Any     		=   [[0] * (w + 1) for _ in range(n + 1 )]

  for i in range(1			,			n + 1 ):
    for w_ in range(1			,			w + 1 ):
      if wt[i - 1] <= w_:
        lowercase__	: List[Any]     		=   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]			,			dp[i - 1][w_] )
      else:
        lowercase__	: Tuple     		=   dp[i - 1][w_]

  return dp[n][w_], dp

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  if not (isinstance(lowercase_			,			(list, tuple) ) and isinstance(lowercase_			,			(list, tuple) )):
    raise ValueError(
        """Both the weights and values vectors must be either lists or tuples""" )

  lowercase__	: str     		=   len(lowercase_ )
  if num_items != len(lowercase_ ):
    lowercase__	: Optional[int]     		=   (
        """The number of weights must be the same as the number of values.\n"""
        F'But got {num_items} weights and {len(lowercase_ )} values'
    )
    raise ValueError(lowercase_ )
  for i in range(lowercase_ ):
    if not isinstance(wt[i]			,			lowercase_ ):
      lowercase__	: int     		=   (
          """All weights must be integers but got weight of """
          F'type {type(wt[i] )} at index {i}'
      )
      raise TypeError(lowercase_ )

  lowercase__		,     lowercase__	: Tuple     		=   knapsack(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )
  lowercase__	: set     		=   set()
  _construct_solution(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )

  return optimal_val, example_optional_set

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Any:


  '''simple docstring'''






  if i > 0 and j > 0:
    if dp[i - 1][j] == dp[i][j]:
      _construct_solution(lowercase_			,			lowercase_			,			i - 1			,			lowercase_			,			lowercase_ )
    else:
      optimal_set.add(lowercase_ )
      _construct_solution(lowercase_			,			lowercase_			,			i - 1			,			j - wt[i - 1]			,			lowercase_ )


if __name__ == "__main__":
     lowerCamelCase__     :		Dict        = [3, 2, 4, 4]
     lowerCamelCase__     :		List[Any]        = [4, 3, 2, 3]
     lowerCamelCase__     :		Optional[int]        = 4
     lowerCamelCase__     :		Dict        = 6
     lowerCamelCase__     :		Optional[int]        = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     lowerCamelCase__   ,    lowerCamelCase__     :		int        = knapsack(w, wt, val, n)
     print(optimal_solution)
     print(mf_knapsack(n, wt, val, w))  # switched the n and w

     # testing the dynamic programming problem with example
     # the optimal subset for the above example are items 3 and 4
     lowerCamelCase__   ,    lowerCamelCase__     :		Optional[int]        = knapsack_with_example_solution(w, wt, val)
     assert optimal_solution == 8
     assert optimal_subset == {3, 4}
     print("""optimal_value = """, optimal_solution)
     print("""An optimal subset corresponding to the optimal value""", optimal_subset)






