import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 		a__						(					A__    ,		unittest.TestCase					):
					A	       =							LEDTokenizer
					A	       =							LEDTokenizerFast
					A	       =							True

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							super().setUp()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    dict(zip(_A,range(len(_A    )    )    )    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    {"unk_token": "<unk>"}

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["vocab_file"]    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    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 				__UpperCamelCase   (    self      :       Tuple,**_A      :       List[str]    ):







							"""simple docstring"""






							kwargs.update(self.special_tokens_map    )
							return self.tokenizer_class.from_pretrained(self.tmpdirname,**_A    )

					def 				__UpperCamelCase   (    self      :       str,**_A      :       Union[str, Any]    ):







							"""simple docstring"""






							kwargs.update(self.special_tokens_map    )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,**_A    )

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       Any    ):







							"""simple docstring"""






							return "lower newer", "lower newer"

					@cached_property
					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							return LEDTokenizer.from_pretrained("allenai/led-base-16384"    )

					@cached_property
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tokenizer(_A,max_length=len(_A    ),padding=_A,return_tensors="pt"    )
									self.assertIsInstance(_A,_A    )

									self.assertEqual((2, 9),batch.input_ids.shape    )
									self.assertEqual((2, 9),batch.attention_mask.shape    )
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    batch.input_ids.tolist()[0]
									self.assertListEqual(_A,_A    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE_  :		int				      =    tokenizer(_A,padding=_A,return_tensors="pt"    )
									self.assertIn("input_ids",_A    )
									self.assertIn("attention_mask",_A    )
									self.assertNotIn("labels",_A    )
									self.assertNotIn("decoder_attention_mask",_A    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    [
							    "Summary of the text.",
							    "Another summary.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tokenizer(text_target=_A,max_length=32,padding="max_length",return_tensors="pt"    )
									self.assertEqual(32,targets["input_ids"].shape[1]    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    tokenizer(
									    ["I am a small frog" * 1024, "I am a small frog"],padding=_A,truncation=_A,return_tensors="pt"    )
									self.assertIsInstance(_A,_A    )
									self.assertEqual(batch.input_ids.shape,(2, 5122)    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    ["A long paragraph for summarization."]
							SCREAMING_SNAKE_CASE_  :		str				      =    [
							    "Summary of the text.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tokenizer(_A,return_tensors="pt"    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tokenizer(text_target=_A,return_tensors="pt"    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    inputs["input_ids"]
									SCREAMING_SNAKE_CASE_  :		Any				      =    targets["input_ids"]
									self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
									self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
									self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
									self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE_  :		str				      =    ["Summary of the text.", "Another summary."]
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tokenizer(_A,padding=_A    )
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [[0] * len(_A    ) for x in encoded_output["input_ids"]]
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tokenizer.pad(_A    )
									self.assertSequenceEqual(outputs["global_attention_mask"],_A    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							pass






					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.rust_tokenizer_class.from_pretrained(_A,**_A    )
											SCREAMING_SNAKE_CASE_  :		Dict				      =    self.tokenizer_class.from_pretrained(_A,**_A    )
											SCREAMING_SNAKE_CASE_  :		Any				      =    "A, <mask> AllenNLP sentence."
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    tokenizer_r.encode_plus(_A,add_special_tokens=_A,return_token_type_ids=_A    )
											SCREAMING_SNAKE_CASE_  :		Any				      =    tokenizer_p.encode_plus(_A,add_special_tokens=_A,return_token_type_ids=_A    )
											self.assertEqual(sum(tokens_r["token_type_ids"]    ),sum(tokens_p["token_type_ids"]    )    )
											self.assertEqual(
											    sum(tokens_r["attention_mask"]    ) / len(tokens_r["attention_mask"]    ),sum(tokens_p["attention_mask"]    ) / len(tokens_p["attention_mask"]    ),)

											SCREAMING_SNAKE_CASE_  :		Dict				      =    tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]    )
											SCREAMING_SNAKE_CASE_  :		str				      =    tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]    )
											self.assertSequenceEqual(tokens_p["input_ids"],[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]    )
											self.assertSequenceEqual(tokens_r["input_ids"],[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]    )

											self.assertSequenceEqual(
											    _A,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )
											self.assertSequenceEqual(
											    _A,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )

from collections import defaultdict




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any				      =    1
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    True
		for v in tree[start]:
				if v not in visited:
						ret += dfs(lowerCAmelCase	)
		if ret % 2 == 0:
				cuts.append(lowerCAmelCase	)
		return ret




def       _snake_case	(   ):
		"""simple docstring"""







		dfs(1	)


if __name__ == "__main__":
		__lowerCamelCase   ,						__lowerCamelCase					:	Union[str, Any]       =  10, 9
		__lowerCamelCase					:	Optional[int]       =  defaultdict(list)
		__lowerCamelCase					:	dict[int, bool]       =  {}
		__lowerCamelCase					:	list[int]       =  []
		__lowerCamelCase					:	Optional[Any]       =  0
		__lowerCamelCase					:	Any       =  [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
		for u, v in edges:
				tree[u].append(v)
				tree[v].append(u)
		even_tree()
		print(len(cuts) - 1)
