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 					_lowercase    (     UpperCAmelCase__ ,  unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				List[Any]          =    CanineTokenizer
    SCREAMING_SNAKE_CASE:				Tuple          =    False




    def      _a    (    self ):
       super().setUp()
       lowerCAmelCase_:							Tuple       =	CanineTokenizer()
       tokenizer.save_pretrained(self.tmpdirname )




    @cached_property
    def      _a    (    self ):
       return CanineTokenizer.from_pretrained("google/canine-s" )




    def      _a    (    self       ,						**lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	self.tokenizer_class.from_pretrained(self.tmpdirname       ,						**A_ )
       lowerCAmelCase_:							Any       =	1_024
       return tokenizer




    @require_torch
    def      _a    (    self ):
       lowerCAmelCase_:							Optional[Any]       =	self.canine_tokenizer
       lowerCAmelCase_:							Optional[Any]       =	["Life is like a box of chocolates.", "You never know what you're gonna get."]
       # fmt: off
       lowerCAmelCase_:							Optional[int]       =	[57_344, 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, 57_345, 0, 0, 0, 0]
       # fmt: on
       lowerCAmelCase_:							List[str]       =	tokenizer(A_       ,						padding=A_       ,						return_tensors="pt" )
       self.assertIsInstance(A_       ,						A_ )

       lowerCAmelCase_:							str       =	list(batch.input_ids.numpy()[0] )

       self.assertListEqual(A_       ,						A_ )

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




    @require_torch
    def      _a    (    self ):
       lowerCAmelCase_:							List[Any]       =	self.canine_tokenizer
       lowerCAmelCase_:							Union[str, Any]       =	["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
       lowerCAmelCase_:							List[str]       =	tokenizer(A_       ,						padding=A_       ,						return_tensors="pt" )
       # check if input_ids, attention_mask and token_type_ids are returned
       self.assertIn("input_ids"       ,						A_ )
       self.assertIn("attention_mask"       ,						A_ )
       self.assertIn("token_type_ids"       ,						A_ )




    @require_torch
    def      _a    (    self ):
       lowerCAmelCase_:							int       =	self.canine_tokenizer
       lowerCAmelCase_:							Optional[int]       =	[
           "What's the weater?",
           "It's about 25 degrees.",
       ]
       lowerCAmelCase_:							List[Any]       =	tokenizer(
           text_target=A_       ,						max_length=32       ,						padding="max_length"       ,						truncation=A_       ,						return_tensors="pt" )
       self.assertEqual(32       ,						targets["input_ids"].shape[1] )




    def      _a    (    self ):
       # safety check on max_len default value so we are sure the test works
       lowerCAmelCase_:							List[str]       =	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
       lowerCAmelCase_:							List[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
             lowerCAmelCase_:							Any       =	tempfile.mkdtemp()

             lowerCAmelCase_:							Optional[int]       =	" He is very happy, UNwant\u00E9d,running"
             lowerCAmelCase_:							str       =	tokenizer.encode(A_       ,						add_special_tokens=A_ )
             tokenizer.save_pretrained(A_ )

             lowerCAmelCase_:							Optional[int]       =	tokenizer.__class__.from_pretrained(A_ )
             lowerCAmelCase_:							List[str]       =	after_tokenizer.encode(A_       ,						add_special_tokens=A_ )
             self.assertListEqual(A_       ,						A_ )

             shutil.rmtree(A_ )

       lowerCAmelCase_:							Optional[Any]       =	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
             lowerCAmelCase_:							int       =	tempfile.mkdtemp()

             lowerCAmelCase_:							Union[str, Any]       =	" He is very happy, UNwant\u00E9d,running"

             lowerCAmelCase_:							List[Any]       =	tokenizer.additional_special_tokens

             # We can add a new special token for Canine as follows:
             lowerCAmelCase_:							Tuple       =	chr(0xE_007 )
             additional_special_tokens.append(A_ )
             tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens} )
             lowerCAmelCase_:							Tuple       =	tokenizer.encode(A_       ,						add_special_tokens=A_ )
             tokenizer.save_pretrained(A_ )

             lowerCAmelCase_:							Optional[int]       =	tokenizer.__class__.from_pretrained(A_ )
             lowerCAmelCase_:							int       =	after_tokenizer.encode(A_       ,						add_special_tokens=A_ )
             self.assertListEqual(A_       ,						A_ )
             self.assertIn(A_       ,						after_tokenizer.additional_special_tokens )
             self.assertEqual(after_tokenizer.model_max_length       ,						42 )

             lowerCAmelCase_:							int       =	tokenizer.__class__.from_pretrained(A_       ,						model_max_length=43 )
             self.assertEqual(tokenizer.model_max_length       ,						43 )

             shutil.rmtree(A_ )




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.get_tokenizers(do_lower_case=A_ )
       for tokenizer in tokenizers:
          with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
             lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[int]       =	self.get_clean_sequence(A_ )

             # a special token for Canine can be defined as follows:
             lowerCAmelCase_:							List[str]       =	0xE_005
             lowerCAmelCase_:							Union[str, Any]       =	chr(A_ )

             tokenizer.add_special_tokens({"cls_token": special_token} )
             lowerCAmelCase_:							str       =	tokenizer.encode(A_       ,						add_special_tokens=A_ )
             self.assertEqual(len(A_ )       ,						1 )

             lowerCAmelCase_:							Any       =	tokenizer.decode(ids + encoded_special_token       ,						clean_up_tokenization_spaces=A_ )
             lowerCAmelCase_:							List[Any]       =	tokenizer.encode(A_       ,						add_special_tokens=A_ )

             lowerCAmelCase_:							List[str]       =	tokenizer.encode(A_       ,						add_special_tokens=A_ )
             lowerCAmelCase_:							List[Any]       =	tokenizer.encode(A_       ,						add_special_tokens=A_ )
             self.assertEqual(A_       ,						input_encoded + special_token_id )

             lowerCAmelCase_:							Optional[Any]       =	tokenizer.decode(A_       ,						skip_special_tokens=A_ )
             self.assertTrue(special_token not in decoded )




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	self.get_tokenizers(do_lower_case=A_ )
       for tokenizer in tokenizers:
          with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
             lowerCAmelCase_:							Optional[int]       =	chr(0xE_005 )
             lowerCAmelCase_:							List[str]       =	chr(0xE_006 )

             # `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=A_ )
             # `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]} )

             lowerCAmelCase_:							Any       =	tokenizer.tokenize(A_ )
             lowerCAmelCase_:							Tuple       =	tokenizer.tokenize(A_ )

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




    @require_tokenizers
    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.get_tokenizers(do_lower_case=A_ )
       for tokenizer in tokenizers:
          with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
             # a special token for Canine can be defined as follows:
             lowerCAmelCase_:							Union[str, Any]       =	0xE_006
             lowerCAmelCase_:							Any       =	chr(A_ )

             lowerCAmelCase_:							List[Any]       =	AddedToken(A_       ,						lstrip=A_ )
             tokenizer.add_special_tokens({"additional_special_tokens": [new_token]} )

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




    def      _a    (    self ):
       lowerCAmelCase_:							Dict       =	[]
       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(A_ )

             with open(os.path.join(A_       ,						"special_tokens_map.json" )       ,						encoding="utf-8" ) as json_file:
                lowerCAmelCase_:							Any       =	json.load(A_ )

             with open(os.path.join(A_       ,						"tokenizer_config.json" )       ,						encoding="utf-8" ) as json_file:
                lowerCAmelCase_:							Tuple       =	json.load(A_ )

             # a special token for Canine can be defined as follows:
             lowerCAmelCase_:							List[Any]       =	0xE_006
             lowerCAmelCase_:							Dict       =	chr(A_ )

             lowerCAmelCase_:							Dict       =	[new_token_a]
             lowerCAmelCase_:							int       =	[new_token_a]

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

             # 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
             lowerCAmelCase_:							Union[str, Any]       =	tokenizer_class.from_pretrained(A_       ,						extra_ids=0 )
             self.assertIn(A_       ,						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] ) )       ,						)

             lowerCAmelCase_:							List[Any]       =	0xE_007
             lowerCAmelCase_:							Tuple       =	chr(A_ )
             # Now we test that we can change the value of additional_special_tokens in the from_pretrained
             lowerCAmelCase_:							Dict       =	[AddedToken(A_       ,						lstrip=A_ )]
             lowerCAmelCase_:							Any       =	tokenizer_class.from_pretrained(
                 A_       ,						additional_special_tokens=A_       ,						extra_ids=0 )

             self.assertIn(A_       ,						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 ):
       lowerCAmelCase_:							Union[str, Any]       =	self.get_tokenizers(do_lower_case=A_ )
       for tokenizer in tokenizers:
          with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
             lowerCAmelCase_:							Optional[int]       =	"hello world"
             if self.space_between_special_tokens:
                lowerCAmelCase_:							Any       =	"[CLS] hello world [SEP]"
             else:
                lowerCAmelCase_:							str       =	input
             lowerCAmelCase_:							List[str]       =	tokenizer.encode(A_       ,						add_special_tokens=A_ )
             lowerCAmelCase_:							Any       =	tokenizer.decode(A_       ,						spaces_between_special_tokens=self.space_between_special_tokens )
             self.assertIn(A_       ,						[output, output.lower()] )




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

       lowerCAmelCase_:							Optional[Any]       =	"a"
       lowerCAmelCase_:							Any       =	ord(A_ )

       for attr in attributes_list:
          setattr(A_       ,						attr + "_id"       ,						A_ )
          self.assertEqual(getattr(A_       ,						A_ )       ,						A_ )
          self.assertEqual(getattr(A_       ,						attr + "_id" )       ,						A_ )

          setattr(A_       ,						attr + "_id"       ,						A_ )
          self.assertEqual(getattr(A_       ,						A_ )       ,						A_ )
          self.assertEqual(getattr(A_       ,						attr + "_id" )       ,						A_ )

       setattr(A_       ,						"additional_special_tokens_ids"       ,						[] )
       self.assertListEqual(getattr(A_       ,						"additional_special_tokens" )       ,						[] )
       self.assertListEqual(getattr(A_       ,						"additional_special_tokens_ids" )       ,						[] )

       lowerCAmelCase_:							int       =	0xE_006
       lowerCAmelCase_:							List[str]       =	chr(A_ )
       setattr(A_       ,						"additional_special_tokens_ids"       ,						[additional_special_token_id] )
       self.assertListEqual(getattr(A_       ,						"additional_special_tokens" )       ,						[additional_special_token] )
       self.assertListEqual(getattr(A_       ,						"additional_special_tokens_ids" )       ,						[additional_special_token_id] )




    def      _a    (    self ):
       pass




    def      _a    (    self ):
       pass




    def      _a    (    self ):
       pass




    def      _a    (    self ):
       pass




    def      _a    (    self ):
       pass




    def      _a    (    self ):
       pass




    def      _a    (    self ):
       pass




    def      _a    (    self ):
       pass



"""simple docstring"""




from collections import defaultdict





def       a_ (	lowercase__ :int     ):
			__lowerCamelCase          =      1
			__lowerCamelCase          =      True
			for v in tree[start]:
						if v not in visited:
									ret += dfs(lowercase__     )
			if ret % 2 == 0:
						cuts.append(lowercase__     )
			return ret





def       a_ (	):
			dfs(1     )


if __name__ == "__main__":
					__magic_name__  , __magic_name__						: Tuple    =				1_0, 9
					__magic_name__						: Tuple    =				defaultdict(list)
					__magic_name__						: dict[int, bool]    =				{}
					__magic_name__						: list[int]    =				[]
					__magic_name__						: List[str]    =				0
					__magic_name__						: Tuple    =				[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (1_0, 8)]
					for u, v in edges:
										tree[u].append(v)
										tree[v].append(u)
					even_tree()
					print(len(cuts) - 1)




