





import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				Union[str, Any]							=    GPTaTokenizer
      A_						:				List[Any]							=    GPTaTokenizerFast
      A_						:				Dict							=    True
      A_						:				List[str]							=    {'add_prefix_space': True}
      A_						:				List[str]							=    False


      def    a (self       :      Any      ):





         """simple docstring"""



         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __snake_case							       =   [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''\u0120''',
             '''\u0120l''',
             '''\u0120n''',
             '''\u0120lo''',
             '''\u0120low''',
             '''er''',
             '''\u0120lowest''',
             '''\u0120newer''',
             '''\u0120wider''',
             '''<unk>''',
             '''<|endoftext|>''',
         ]
         __snake_case							       =   dict(zip(a__				,			range(len(a__      )      )      )      )
         __snake_case							       =   ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
         __snake_case							       =   {'''unk_token''': '''<unk>'''}

         __snake_case							       =   os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''vocab_file''']      )
         __snake_case							       =   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       :      List[str]				,			**a__       :      List[Any]      ):





         """simple docstring"""



         kwargs.update(self.special_tokens_map      )
         return GPTaTokenizer.from_pretrained(self.tmpdirname				,			**a__      )


      def    a (self       :      Dict				,			**a__       :      Union[str, Any]      ):





         """simple docstring"""



         kwargs.update(self.special_tokens_map      )
         return GPTaTokenizerFast.from_pretrained(self.tmpdirname				,			**a__      )


      def    a (self       :      int				,			a__       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   '''lower newer'''
         __snake_case							       =   '''lower newer'''
         return input_text, output_text


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   GPTaTokenizer(self.vocab_file				,			self.merges_file				,			**self.special_tokens_map      )
         __snake_case							       =   '''lower newer'''
         __snake_case							       =   ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         __snake_case							       =   tokenizer.tokenize(a__				,			add_prefix_space=a__      )
         self.assertListEqual(a__				,			a__      )

         __snake_case							       =   tokens + [tokenizer.unk_token]
         __snake_case							       =   [14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__      )				,			a__      )


      def    a (self       :      int      ):





         """simple docstring"""



         if not self.test_rust_tokenizer:
            return

         __snake_case							       =   self.get_tokenizer()
         __snake_case							       =   self.get_rust_tokenizer(add_prefix_space=a__      )

         __snake_case							       =   '''lower newer'''

         # Testing tokenization
         __snake_case							       =   tokenizer.tokenize(a__				,			add_prefix_space=a__      )
         __snake_case							       =   rust_tokenizer.tokenize(a__      )
         self.assertListEqual(a__				,			a__      )

         # Testing conversion to ids without special tokens
         __snake_case							       =   tokenizer.encode(a__				,			add_special_tokens=a__				,			add_prefix_space=a__      )
         __snake_case							       =   rust_tokenizer.encode(a__				,			add_special_tokens=a__      )
         self.assertListEqual(a__				,			a__      )

         # Testing conversion to ids with special tokens
         __snake_case							       =   self.get_rust_tokenizer(add_prefix_space=a__      )
         __snake_case							       =   tokenizer.encode(a__				,			add_prefix_space=a__      )
         __snake_case							       =   rust_tokenizer.encode(a__      )
         self.assertListEqual(a__				,			a__      )

         # Testing the unknown token
         __snake_case							       =   tokens + [rust_tokenizer.unk_token]
         __snake_case							       =   [14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a__      )				,			a__      )


      def    a (self       :      Tuple				,			*a__       :      Optional[int]				,			**a__       :      Optional[Any]      ):





         """simple docstring"""



         pass


      def    a (self       :      List[str]				,			a__       :      Any=15      ):





         """simple docstring"""



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
               __snake_case							       =   self.rust_tokenizer_class.from_pretrained(a__				,			**a__      )

               # Simple input
               __snake_case							       =   '''This is a simple input'''
               __snake_case							       =   ['''This is a simple input 1''', '''This is a simple input 2''']
               __snake_case							       =   ('''This is a simple input''', '''This is a pair''')
               __snake_case							       =   [
                   ('''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(a__				,			tokenizer_r.encode				,			a__				,			max_length=a__				,			padding='''max_length'''      )

               # Simple input
               self.assertRaises(a__				,			tokenizer_r.encode_plus				,			a__				,			max_length=a__				,			padding='''max_length'''      )

               # Simple input
               self.assertRaises(
                   a__				,			tokenizer_r.batch_encode_plus				,			a__				,			max_length=a__				,			padding='''max_length'''				,			)

               # Pair input
               self.assertRaises(a__				,			tokenizer_r.encode				,			a__				,			max_length=a__				,			padding='''max_length'''      )

               # Pair input
               self.assertRaises(a__				,			tokenizer_r.encode_plus				,			a__				,			max_length=a__				,			padding='''max_length'''      )

               # Pair input
               self.assertRaises(
                   a__				,			tokenizer_r.batch_encode_plus				,			a__				,			max_length=a__				,			padding='''max_length'''				,			)


      def    a (self       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   GPTaTokenizer.from_pretrained(self.tmpdirname				,			pad_token='''<pad>'''      )

         # Simple input
         __snake_case							       =   '''This is a simple input'''
         __snake_case							       =   ['''This is a simple input looooooooong''', '''This is a simple input''']
         __snake_case							       =   ('''This is a simple input''', '''This is a pair''')
         __snake_case							       =   [
             ('''This is a simple input loooooong''', '''This is a simple input'''),
             ('''This is a simple pair loooooong''', '''This is a simple pair'''),
         ]

         __snake_case							       =   tokenizer.pad_token_id

         __snake_case							       =   tokenizer(a__				,			padding='''max_length'''				,			max_length=30				,			return_tensors='''np'''      )
         __snake_case							       =   tokenizer(a__				,			padding=a__				,			truncate=a__				,			return_tensors='''np'''      )
         __snake_case							       =   tokenizer(*a__				,			padding='''max_length'''				,			max_length=60				,			return_tensors='''np'''      )
         __snake_case							       =   tokenizer(a__				,			padding=a__				,			truncate=a__				,			return_tensors='''np'''      )

         # s
         # test single string max_length padding
         self.assertEqual(out_s['''input_ids'''].shape[-1]				,			30      )
         self.assertTrue(pad_token_id in out_s['''input_ids''']      )
         self.assertTrue(0 in out_s['''attention_mask''']      )

         # s2
         # test automatic padding
         self.assertEqual(out_sa['''input_ids'''].shape[-1]				,			33      )
         # long slice doesn't have padding
         self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]      )
         self.assertFalse(0 in out_sa['''attention_mask'''][0]      )
         # short slice does have padding
         self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]      )
         self.assertTrue(0 in out_sa['''attention_mask'''][1]      )

         # p
         # test single pair max_length padding
         self.assertEqual(out_p['''input_ids'''].shape[-1]				,			60      )
         self.assertTrue(pad_token_id in out_p['''input_ids''']      )
         self.assertTrue(0 in out_p['''attention_mask''']      )

         # p2
         # test automatic padding pair
         self.assertEqual(out_pa['''input_ids'''].shape[-1]				,			52      )
         # long slice pair doesn't have padding
         self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]      )
         self.assertFalse(0 in out_pa['''attention_mask'''][0]      )
         # short slice pair does have padding
         self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]      )
         self.assertTrue(0 in out_pa['''attention_mask'''][1]      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   '''$$$'''
         __snake_case							       =   GPTaTokenizer.from_pretrained(self.tmpdirname				,			bos_token=a__				,			add_bos_token=a__      )

         __snake_case							       =   '''This is a simple input'''
         __snake_case							       =   ['''This is a simple input 1''', '''This is a simple input 2''']

         __snake_case							       =   tokenizer.bos_token_id

         __snake_case							       =   tokenizer(a__      )
         __snake_case							       =   tokenizer(a__      )

         self.assertEqual(out_s.input_ids[0]				,			a__      )
         self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids      )      )

         __snake_case							       =   tokenizer.decode(out_s.input_ids      )
         __snake_case							       =   tokenizer.batch_decode(out_sa.input_ids      )

         self.assertEqual(decode_s.split()[0]				,			a__      )
         self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa      )      )


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         pass


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   [self.get_tokenizer(do_lower_case=a__				,			add_bos_token=a__      )]
         for tokenizer in tokenizers:
            with self.subTest(f"""{tokenizer.__class__.__name__}"""      ):
               __snake_case							       =   '''Encode this.'''
               __snake_case							       =   '''This one too please.'''
               __snake_case							       =   tokenizer.encode(a__				,			add_special_tokens=a__      )
               encoded_sequence += tokenizer.encode(a__				,			add_special_tokens=a__      )
               __snake_case							       =   tokenizer.encode_plus(
                   a__				,			a__				,			add_special_tokens=a__				,			return_special_tokens_mask=a__				,			)
               __snake_case							       =   encoded_sequence_dict['''input_ids''']
               __snake_case							       =   encoded_sequence_dict['''special_tokens_mask''']
               self.assertEqual(len(a__      )				,			len(a__      )      )

               __snake_case							       =   [
                   (x if not special_tokens_mask[i] else None) for i, x in enumerate(a__      )
               ]
               __snake_case							       =   [x for x in filtered_sequence if x is not None]
               self.assertEqual(a__				,			a__      )






@require_tokenizers
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   AutoTokenizer.from_pretrained('''facebook/opt-350m'''				,			from_slow=a__      )
         __snake_case							       =   '''A photo of a cat'''

         __snake_case							       =   tokenizer.encode(
             a__				,			)
         self.assertEqual(a__				,			[2, 250, 1345, 9, 10, 4758]      )
         tokenizer.save_pretrained('''test_opt'''      )

         __snake_case							       =   AutoTokenizer.from_pretrained('''./test_opt'''      )
         __snake_case							       =   tokenizer.encode(
             a__				,			)
         self.assertEqual(a__				,			[2, 250, 1345, 9, 10, 4758]      )


      def    a (self       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   AutoTokenizer.from_pretrained('''facebook/opt-350m'''				,			use_slow=a__      )
         __snake_case							       =   '''A photo of a cat'''

         __snake_case							       =   tokenizer.encode(
             a__				,			)
         # Same as above
         self.assertEqual(a__				,			[2, 250, 1345, 9, 10, 4758]      )


      @unittest.skip('''This test is failing because of a bug in the fast tokenizer'''      )
      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   AutoTokenizer.from_pretrained('''facebook/opt-350m'''				,			from_slow=a__      )

         __snake_case							       =   '''bos'''
         __snake_case							       =   tokenizer.get_vocab()['''bos''']

         __snake_case							       =   '''A photo of a cat'''
         __snake_case							       =   tokenizer.encode(
             a__				,			)
         # We changed the bos token
         self.assertEqual(a__				,			[3_1957, 250, 1345, 9, 10, 4758]      )
         tokenizer.save_pretrained('''./tok'''      )
         __snake_case							       =   AutoTokenizer.from_pretrained('''./tok'''      )
         self.assertTrue(tokenizer.is_fast      )
         __snake_case							       =   tokenizer.encode(
             a__				,			)
         self.assertEqual(a__				,			[3_1957, 250, 1345, 9, 10, 4758]      )




def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    [[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_:
																												snake_case_							=    max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]   ,							dp[i - 1][w_]      )
																					else:
																												snake_case_							=    dp[i - 1][w_]

							return dp[n][w_], dp





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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

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

							snake_case_				,    snake_case_							=    knapsack(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							snake_case_							=    set()
							_construct_solution(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )

							return optimal_val, example_optional_set





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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


if __name__ == "__main__":
					_UpperCAmelCase		:   Tuple										=       [3, 2, 4, 4]
					_UpperCAmelCase		:   Optional[Any]										=       [4, 3, 2, 3]
					_UpperCAmelCase		:   List[str]										=       4
					_UpperCAmelCase		:   str										=       6
					_UpperCAmelCase		:   Tuple										=       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					_UpperCAmelCase				,						_UpperCAmelCase		:   List[Any]										=       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
					_UpperCAmelCase				,						_UpperCAmelCase		:   Any										=       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)




