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     _A   (    __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						=CanineTokenizer
  SCREAMING_SNAKE_CASE_	:	List[Any]						=False






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       super().setUp()
       UpperCamelCase__										=      CanineTokenizer()
       tokenizer.save_pretrained(self.tmpdirname	)






  @cached_property
  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       return CanineTokenizer.from_pretrained('''google/canine-s'''	)






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      CanineTokenizer:





       '''simple docstring'''





       UpperCamelCase__										=      self.tokenizer_class.from_pretrained(self.tmpdirname   ,				**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      1024
       return tokenizer






  @require_torch
  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      self.canine_tokenizer
       UpperCamelCase__										=      ['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
       # fmt: off
       UpperCamelCase__										=      [5_7344, 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, 5_7345, 0, 0, 0, 0]
       # fmt: on
       UpperCamelCase__										=      tokenizer(SCREAMING_SNAKE_CASE_   ,				padding=SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	)
       self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      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	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.canine_tokenizer
       UpperCamelCase__										=      ['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
       UpperCamelCase__										=      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[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.canine_tokenizer
       UpperCamelCase__										=      [
           '''What\'s the weater?''',
           '''It\'s about 25 degrees.''',
       ]
       UpperCamelCase__										=      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	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      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
       UpperCamelCase__										=      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
                 UpperCamelCase__										=      tempfile.mkdtemp()

                 UpperCamelCase__										=      ''' He is very happy, UNwant\u00E9d,running'''
                 UpperCamelCase__										=      tokenizer.encode(SCREAMING_SNAKE_CASE_   ,				add_special_tokens=SCREAMING_SNAKE_CASE_	)
                 tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_	)

                 UpperCamelCase__										=      tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      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_	)

       UpperCamelCase__										=      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
                 UpperCamelCase__										=      tempfile.mkdtemp()

                 UpperCamelCase__										=      ''' He is very happy, UNwant\u00E9d,running'''

                 UpperCamelCase__										=      tokenizer.additional_special_tokens

                 # We can add a new special token for Canine as follows:
                 UpperCamelCase__										=      chr(0xE0_07	)
                 additional_special_tokens.append(SCREAMING_SNAKE_CASE_	)
                 tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}	)
                 UpperCamelCase__										=      tokenizer.encode(SCREAMING_SNAKE_CASE_   ,				add_special_tokens=SCREAMING_SNAKE_CASE_	)
                 tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_	)

                 UpperCamelCase__										=      tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      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	)

                 UpperCamelCase__										=      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	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE_	)
       for tokenizer in tokenizers:
            with self.subTest(F"{tokenizer.__class__.__name__}"	):
                 UpperCamelCase__						,       UpperCamelCase__										=      self.get_clean_sequence(SCREAMING_SNAKE_CASE_	)

                 # a special token for Canine can be defined as follows:
                 UpperCamelCase__										=      0xE0_05
                 UpperCamelCase__										=      chr(SCREAMING_SNAKE_CASE_	)

                 tokenizer.add_special_tokens({'''cls_token''': special_token}	)
                 UpperCamelCase__										=      tokenizer.encode(SCREAMING_SNAKE_CASE_   ,				add_special_tokens=SCREAMING_SNAKE_CASE_	)
                 self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				1	)

                 UpperCamelCase__										=      tokenizer.decode(ids + encoded_special_token   ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      tokenizer.encode(SCREAMING_SNAKE_CASE_   ,				add_special_tokens=SCREAMING_SNAKE_CASE_	)

                 UpperCamelCase__										=      tokenizer.encode(SCREAMING_SNAKE_CASE_   ,				add_special_tokens=SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      tokenizer.encode(SCREAMING_SNAKE_CASE_   ,				add_special_tokens=SCREAMING_SNAKE_CASE_	)
                 self.assertEqual(SCREAMING_SNAKE_CASE_   ,				input_encoded + special_token_id	)

                 UpperCamelCase__										=      tokenizer.decode(SCREAMING_SNAKE_CASE_   ,				skip_special_tokens=SCREAMING_SNAKE_CASE_	)
                 self.assertTrue(special_token not in decoded	)






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE_	)
       for tokenizer in tokenizers:
            with self.subTest(F"{tokenizer.__class__.__name__}"	):
                 UpperCamelCase__										=      chr(0xE0_05	)
                 UpperCamelCase__										=      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]}	)

                 UpperCamelCase__										=      tokenizer.tokenize(SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      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	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      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:
                 UpperCamelCase__										=      0xE0_06
                 UpperCamelCase__										=      chr(SCREAMING_SNAKE_CASE_	)

                 UpperCamelCase__										=      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	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      []
       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:
                      UpperCamelCase__										=      json.load(SCREAMING_SNAKE_CASE_	)

                 with open(os.path.join(SCREAMING_SNAKE_CASE_   ,				'''tokenizer_config.json'''	)   ,				encoding='''utf-8'''	) as json_file:
                      UpperCamelCase__										=      json.load(SCREAMING_SNAKE_CASE_	)

                 # a special token for Canine can be defined as follows:
                 UpperCamelCase__										=      0xE0_06
                 UpperCamelCase__										=      chr(SCREAMING_SNAKE_CASE_	)

                 UpperCamelCase__										=      [new_token_a]
                 UpperCamelCase__										=      [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
                 UpperCamelCase__										=      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]	)	)   ,				)

                 UpperCamelCase__										=      0xE0_07
                 UpperCamelCase__										=      chr(SCREAMING_SNAKE_CASE_	)
                 # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                 UpperCamelCase__										=      [AddedToken(SCREAMING_SNAKE_CASE_   ,				lstrip=SCREAMING_SNAKE_CASE_	)]
                 UpperCamelCase__										=      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	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE_	)
       for tokenizer in tokenizers:
            with self.subTest(F"{tokenizer.__class__.__name__}"	):
                 UpperCamelCase__										=      '''hello world'''
                 if self.space_between_special_tokens:
                      UpperCamelCase__										=      '''[CLS] hello world [SEP]'''
                 else:
                      UpperCamelCase__										=      input
                 UpperCamelCase__										=      tokenizer.encode(SCREAMING_SNAKE_CASE_   ,				add_special_tokens=SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      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	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_tokenizers()
       for tokenizer in tokenizers:
            with self.subTest(F"{tokenizer.__class__.__name__}"	):
                 UpperCamelCase__										=      [
                     '''bos_token''',
                     '''eos_token''',
                     '''unk_token''',
                     '''sep_token''',
                     '''pad_token''',
                     '''cls_token''',
                     '''mask_token''',
                 ]

       UpperCamelCase__										=      '''a'''
       UpperCamelCase__										=      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'''	)   ,				[]	)

       UpperCamelCase__										=      0xE0_06
       UpperCamelCase__										=      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	)		->      Optional[Any]:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       pass






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





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       pass






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





       '''simple docstring'''





       pass




def  __UpperCamelCase    (						A = 600851475143							):

     try:
          UpperCamelCase__										=      int(A							)
     except (TypeError, ValueError):
          raise TypeError('''Parameter n must be int or castable to int.'''							)
     if n <= 0:
          raise ValueError('''Parameter n must be greater than or equal to one.'''							)
     UpperCamelCase__										=      1
     UpperCamelCase__										=      2
     while i * i <= n:
          while n % i == 0:
               UpperCamelCase__										=      i
               n //= i
          i += 1
     if n > 1:
          UpperCamelCase__										=      n
     return int(A							)


if __name__ == "__main__":
     print(f"""{solution() = }""")



