


'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   CanineTokenizer
    a													=   False


    def        A   (  self      :							Optional[int]):
           super().setUp()
           _A					:     Optional[int]     = CanineTokenizer()
           tokenizer.save_pretrained(self.tmpdirname)


    @cached_property
    def        A   (  self      :							Any):
           return CanineTokenizer.from_pretrained('google/canine-s')


    def        A   (  self      :							str				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Optional[int]     = self.tokenizer_class.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE)
           _A					:     Dict     = 1024
           return tokenizer


    @require_torch
    def        A   (  self      :							Dict):
           _A					:     Tuple     = self.canine_tokenizer
           _A					:     Dict     = ['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
           # fmt: off
           _A					:     List[str]     = [57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0]
           # fmt: on
           _A					:     Any     = tokenizer(SCREAMING_SNAKE_CASE				,							padding=SCREAMING_SNAKE_CASE				,							return_tensors='pt')
           self.assertIsInstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           _A					:     int     = list(batch.input_ids.numpy()[0])

           self.assertListEqual(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           self.assertEqual((2, 39)				,							batch.input_ids.shape)
           self.assertEqual((2, 39)				,							batch.attention_mask.shape)


    @require_torch
    def        A   (  self      :							Tuple):
           _A					:     Optional[int]     = self.canine_tokenizer
           _A					:     Optional[int]     = ['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
           _A					:     Optional[int]     = tokenizer(SCREAMING_SNAKE_CASE				,							padding=SCREAMING_SNAKE_CASE				,							return_tensors='pt')
           # check if input_ids, attention_mask and token_type_ids are returned
           self.assertIn('input_ids'				,							SCREAMING_SNAKE_CASE)
           self.assertIn('attention_mask'				,							SCREAMING_SNAKE_CASE)
           self.assertIn('token_type_ids'				,							SCREAMING_SNAKE_CASE)


    @require_torch
    def        A   (  self      :							List[str]):
           _A					:     str     = self.canine_tokenizer
           _A					:     str     = [
               'What\'s the weater?',
               'It\'s about 25 degrees.',
           ]
           _A					:     List[Any]     = tokenizer(
               text_target=SCREAMING_SNAKE_CASE				,							max_length=32				,							padding='max_length'				,							truncation=SCREAMING_SNAKE_CASE				,							return_tensors='pt')
           self.assertEqual(32				,							targets['input_ids'].shape[1])


    def        A   (  self      :							Optional[Any]):
           # safety check on max_len default value so we are sure the test works
           _A					:     Any     = self.get_tokenizers()
           for tokenizer in tokenizers:
                  with self.subTest(F'{tokenizer.__class__.__name__}'):
                         self.assertNotEqual(tokenizer.model_max_length				,							42)

        # Now let's start the test
           _A					:     Optional[Any]     = self.get_tokenizers()
           for tokenizer in tokenizers:
                  with self.subTest(F'{tokenizer.__class__.__name__}'):
                         # Isolate this from the other tests because we save additional tokens/etc
                         _A					:     Tuple     = tempfile.mkdtemp()

                         _A					:     str     = ' He is very happy, UNwant\u00E9d,running'
                         _A					:     Optional[Any]     = tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         tokenizer.save_pretrained(SCREAMING_SNAKE_CASE)

                         _A					:     List[Any]     = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE)
                         _A					:     Union[str, Any]     = after_tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         self.assertListEqual(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

                         shutil.rmtree(SCREAMING_SNAKE_CASE)

           _A					:     Tuple     = self.get_tokenizers(model_max_length=42)
           for tokenizer in tokenizers:
                  with self.subTest(F'{tokenizer.__class__.__name__}'):
                         # Isolate this from the other tests because we save additional tokens/etc
                         _A					:     List[Any]     = tempfile.mkdtemp()

                         _A					:     Optional[Any]     = ' He is very happy, UNwant\u00E9d,running'

                         _A					:     Tuple     = tokenizer.additional_special_tokens

                         # We can add a new special token for Canine as follows:
                         _A					:     Optional[int]     = chr(0xe0_07)
                         additional_special_tokens.append(SCREAMING_SNAKE_CASE)
                         tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens})
                         _A					:     Optional[Any]     = tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         tokenizer.save_pretrained(SCREAMING_SNAKE_CASE)

                         _A					:     Union[str, Any]     = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE)
                         _A					:     Any     = after_tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         self.assertListEqual(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                         self.assertIn(SCREAMING_SNAKE_CASE				,							after_tokenizer.additional_special_tokens)
                         self.assertEqual(after_tokenizer.model_max_length				,							42)

                         _A					:     Dict     = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE				,							model_max_length=43)
                         self.assertEqual(tokenizer.model_max_length				,							43)

                         shutil.rmtree(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     List[Any]     = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE)
           for tokenizer in tokenizers:
                  with self.subTest(F'{tokenizer.__class__.__name__}'):
                         _A  ,			_A					:     Optional[Any]     = self.get_clean_sequence(SCREAMING_SNAKE_CASE)

                         # a special token for Canine can be defined as follows:
                         _A					:     Any     = 0xe0_05
                         _A					:     List[str]     = chr(SCREAMING_SNAKE_CASE)

                         tokenizer.add_special_tokens({'cls_token': special_token})
                         _A					:     Any     = tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         self.assertEqual(len(SCREAMING_SNAKE_CASE)				,							1)

                         _A					:     Optional[int]     = tokenizer.decode(ids + encoded_special_token				,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE)
                         _A					:     Tuple     = tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)

                         _A					:     Optional[Any]     = tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         _A					:     Any     = tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         self.assertEqual(SCREAMING_SNAKE_CASE				,							input_encoded + special_token_id)

                         _A					:     str     = tokenizer.decode(SCREAMING_SNAKE_CASE				,							skip_special_tokens=SCREAMING_SNAKE_CASE)
                         self.assertTrue(special_token not in decoded)


    def        A   (  self      :							List[str]):
           _A					:     Tuple     = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE)
           for tokenizer in tokenizers:
                  with self.subTest(F'{tokenizer.__class__.__name__}'):
                         _A					:     str     = chr(0xe0_05)
                         _A					:     Any     = chr(0xe0_06)

                         # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                         tokenizer.add_tokens([SPECIAL_TOKEN_1]				,							special_tokens=SCREAMING_SNAKE_CASE)
                         # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                         # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                         tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]})

                         _A					:     Tuple     = tokenizer.tokenize(SCREAMING_SNAKE_CASE)
                         _A					:     List[Any]     = tokenizer.tokenize(SCREAMING_SNAKE_CASE)

                         self.assertEqual(len(SCREAMING_SNAKE_CASE)				,							1)
                         self.assertEqual(len(SCREAMING_SNAKE_CASE)				,							1)
                         self.assertEqual(token_a[0]				,							SCREAMING_SNAKE_CASE)
                         self.assertEqual(token_a[0]				,							SCREAMING_SNAKE_CASE)


    @require_tokenizers
    def        A   (  self      :							Union[str, Any]):
           _A					:     int     = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE)
           for tokenizer in tokenizers:
                  with self.subTest(F'{tokenizer.__class__.__name__}'):
                         # a special token for Canine can be defined as follows:
                         _A					:     Optional[Any]     = 0xe0_06
                         _A					:     Union[str, Any]     = chr(SCREAMING_SNAKE_CASE)

                         _A					:     Optional[Any]     = AddedToken(SCREAMING_SNAKE_CASE				,							lstrip=SCREAMING_SNAKE_CASE)
                         tokenizer.add_special_tokens({'additional_special_tokens': [new_token]})

                         with tempfile.TemporaryDirectory() as tmp_dir_name:
                                tokenizer.save_pretrained(SCREAMING_SNAKE_CASE)
                                tokenizer.from_pretrained(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     Optional[int]     = []
           if self.test_slow_tokenizer:
                  tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

           if self.test_rust_tokenizer:
                  tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

           for tokenizer_class, tokenizer_utils in tokenizer_list:
                  with tempfile.TemporaryDirectory() as tmp_dir:
                         tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE)

                         with open(os.path.join(SCREAMING_SNAKE_CASE				,							'special_tokens_map.json')				,							encoding='utf-8') as json_file:
                                _A					:     str     = json.load(SCREAMING_SNAKE_CASE)

                         with open(os.path.join(SCREAMING_SNAKE_CASE				,							'tokenizer_config.json')				,							encoding='utf-8') as json_file:
                                _A					:     Optional[Any]     = json.load(SCREAMING_SNAKE_CASE)

                         # a special token for Canine can be defined as follows:
                         _A					:     List[str]     = 0xe0_06
                         _A					:     str     = chr(SCREAMING_SNAKE_CASE)

                         _A					:     Any     = [new_token_a]
                         _A					:     Tuple     = [new_token_a]

                         with open(os.path.join(SCREAMING_SNAKE_CASE				,							'special_tokens_map.json')				,							'w'				,							encoding='utf-8') as outfile:
                                json.dump(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                         with open(os.path.join(SCREAMING_SNAKE_CASE				,							'tokenizer_config.json')				,							'w'				,							encoding='utf-8') as outfile:
                                json.dump(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

                         # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                         # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                         # "special_tokens_map.json" files
                         _A					:     Optional[Any]     = tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE				,							extra_ids=0)
                         self.assertIn(SCREAMING_SNAKE_CASE				,							tokenizer_without_change_in_init.additional_special_tokens)
                         # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                         self.assertEqual(
                             [new_token_a]				,							tokenizer_without_change_in_init.convert_ids_to_tokens(
                                 tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]))				,							)

                         _A					:     Dict     = 0xe0_07
                         _A					:     List[Any]     = chr(SCREAMING_SNAKE_CASE)
                         # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                         _A					:     Union[str, Any]     = [AddedToken(SCREAMING_SNAKE_CASE				,							lstrip=SCREAMING_SNAKE_CASE)]
                         _A					:     Optional[Any]     = tokenizer_class.from_pretrained(
                             SCREAMING_SNAKE_CASE				,							additional_special_tokens=SCREAMING_SNAKE_CASE				,							extra_ids=0)

                         self.assertIn(SCREAMING_SNAKE_CASE				,							tokenizer.additional_special_tokens)
                         # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                         self.assertEqual(
                             [new_token_a]				,							tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a])))


    @require_tokenizers
    def        A   (  self      :							List[Any]):
           _A					:     List[Any]     = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE)
           for tokenizer in tokenizers:
                  with self.subTest(F'{tokenizer.__class__.__name__}'):
                         _A					:     Tuple     = 'hello world'
                         if self.space_between_special_tokens:
                                _A					:     Union[str, Any]     = '[CLS] hello world [SEP]'
                         else:
                                _A					:     Tuple     = input
                         _A					:     List[Any]     = tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE)
                         _A					:     Any     = tokenizer.decode(SCREAMING_SNAKE_CASE				,							spaces_between_special_tokens=self.space_between_special_tokens)
                         self.assertIn(SCREAMING_SNAKE_CASE				,							[output, output.lower()])


    def        A   (  self      :							Tuple):
           _A					:     Union[str, Any]     = self.get_tokenizers()
           for tokenizer in tokenizers:
                  with self.subTest(F'{tokenizer.__class__.__name__}'):
                         _A					:     Dict     = [
                             'bos_token',
                             'eos_token',
                             'unk_token',
                             'sep_token',
                             'pad_token',
                             'cls_token',
                             'mask_token',
                         ]

           _A					:     Any     = 'a'
           _A					:     int     = ord(SCREAMING_SNAKE_CASE)

           for attr in attributes_list:
                  setattr(SCREAMING_SNAKE_CASE				,							attr + '_id'				,							SCREAMING_SNAKE_CASE)
                  self.assertEqual(getattr(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)				,							SCREAMING_SNAKE_CASE)
                  self.assertEqual(getattr(SCREAMING_SNAKE_CASE				,							attr + '_id')				,							SCREAMING_SNAKE_CASE)

                  setattr(SCREAMING_SNAKE_CASE				,							attr + '_id'				,							SCREAMING_SNAKE_CASE)
                  self.assertEqual(getattr(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)				,							SCREAMING_SNAKE_CASE)
                  self.assertEqual(getattr(SCREAMING_SNAKE_CASE				,							attr + '_id')				,							SCREAMING_SNAKE_CASE)

           setattr(SCREAMING_SNAKE_CASE				,							'additional_special_tokens_ids'				,							[])
           self.assertListEqual(getattr(SCREAMING_SNAKE_CASE				,							'additional_special_tokens')				,							[])
           self.assertListEqual(getattr(SCREAMING_SNAKE_CASE				,							'additional_special_tokens_ids')				,							[])

           _A					:     List[Any]     = 0xe0_06
           _A					:     str     = chr(SCREAMING_SNAKE_CASE)
           setattr(SCREAMING_SNAKE_CASE				,							'additional_special_tokens_ids'				,							[additional_special_token_id])
           self.assertListEqual(getattr(SCREAMING_SNAKE_CASE				,							'additional_special_tokens')				,							[additional_special_token])
           self.assertListEqual(getattr(SCREAMING_SNAKE_CASE				,							'additional_special_tokens_ids')				,							[additional_special_token_id])


    def        A   (  self      :							Tuple):
           pass


    def        A   (  self      :							int):
           pass


    def        A   (  self      :							Optional[Any]):
           pass


    def        A   (  self      :							List[str]):
           pass


    def        A   (  self      :							int):
           pass


    def        A   (  self      :							Any):
           pass


    def        A   (  self      :							Optional[int]):
           pass


    def        A   (  self      :							List[Any]):
           pass










'''simple docstring'''







def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):

       def count_of_possible_combinations(lowerCamelCase					:      int       ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              return sum(count_of_possible_combinations(target - item       ) for item in array       )

       return count_of_possible_combinations(lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):

       def count_of_possible_combinations_with_dp_array(
           lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]       ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              if dp_array[target] != -1:
                     return dp_array[target]
              _A					:     Optional[Any]     = sum(
                  count_of_possible_combinations_with_dp_array(target - item  ,lowerCamelCase       )
                  for item in array       )
              _A					:     List[str]     = answer
              return answer

       _A					:     Optional[int]     = [-1] * (target + 1)
       return count_of_possible_combinations_with_dp_array(lowerCamelCase  ,lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):
       _A					:     Dict     = [0] * (target + 1)
       _A					:     List[str]     = 1

       for i in range(1  ,target + 1       ):
              for j in range(lowerCamelCase       ):
                     if i - array[j] >= 0:
                            dp_array[i] += dp_array[i - array[j]]

       return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   A				:					Dict    						=			3
   A				:					Union[str, Any]    						=			5
   A				:					Union[str, Any]    						=			[1, 2, 5]
   print(combination_sum_iv(n, array, target))






