import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AlignProcessor, EfficientNetImageProcessor






@require_vision
class 		a__						(					unittest.TestCase					):

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    tempfile.mkdtemp()

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [
							    "[UNK]",
							    "[CLS]",
							    "[SEP]",
							    "[PAD]",
							    "[MASK]",
							    "want",
							    "##want",
							    "##ed",
							    "wa",
							    "un",
							    "runn",
							    "##ing",
							    ",",
							    "low",
							    "lowest",
							]
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["vocab_file"]    )
							with open(self.vocab_file,"w",encoding="utf-8"    ) as vocab_writer:
									vocab_writer.write("".join([x + "\n" for x in vocab_tokens]    )    )

							SCREAMING_SNAKE_CASE_  :		str				      =    {
							    "do_resize": True,
							    "size": 20,
							    "do_center_crop": True,
							    "crop_size": 18,
							    "do_normalize": True,
							    "image_mean": [0.48145466, 0.4578275, 0.40821073],
							    "image_std": [0.26862954, 0.26130258, 0.27577711],
							}
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    os.path.join(self.tmpdirname,_A    )
							with open(self.image_processor_file,"w",encoding="utf-8"    ) as fp:
									json.dump(_A,_A    )

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







							"""simple docstring"""






							return BertTokenizer.from_pretrained(self.tmpdirname,**_A    )

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







							"""simple docstring"""






							return BertTokenizerFast.from_pretrained(self.tmpdirname,**_A    )

					def 				__UpperCamelCase   (    self      :       Optional[int],**_A      :       Dict    ):







							"""simple docstring"""






							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname,**_A    )

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







							"""simple docstring"""






							shutil.rmtree(self.tmpdirname    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    [np.random.randint(255,size=(3, 30, 400),dtype=np.uinta    )]
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [Image.fromarray(np.moveaxis(_A,0,-1    )    ) for x in image_inputs]
							return image_inputs

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    self.get_tokenizer()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.get_rust_tokenizer()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.get_image_processor()

							SCREAMING_SNAKE_CASE_  :		Any				      =    AlignProcessor(tokenizer=_A,image_processor=_A    )
							processor_slow.save_pretrained(self.tmpdirname    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    AlignProcessor.from_pretrained(self.tmpdirname,use_fast=_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AlignProcessor(tokenizer=_A,image_processor=_A    )
							processor_fast.save_pretrained(self.tmpdirname    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    AlignProcessor.from_pretrained(self.tmpdirname    )

							self.assertEqual(processor_slow.tokenizer.get_vocab(),tokenizer_slow.get_vocab()    )
							self.assertEqual(processor_fast.tokenizer.get_vocab(),tokenizer_fast.get_vocab()    )
							self.assertEqual(tokenizer_slow.get_vocab(),tokenizer_fast.get_vocab()    )
							self.assertIsInstance(processor_slow.tokenizer,_A    )
							self.assertIsInstance(processor_fast.tokenizer,_A    )

							self.assertEqual(processor_slow.image_processor.to_json_string(),image_processor.to_json_string()    )
							self.assertEqual(processor_fast.image_processor.to_json_string(),image_processor.to_json_string()    )
							self.assertIsInstance(processor_slow.image_processor,_A    )
							self.assertIsInstance(processor_fast.image_processor,_A    )

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    AlignProcessor(tokenizer=self.get_tokenizer(),image_processor=self.get_image_processor()    )
							processor.save_pretrained(self.tmpdirname    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    self.get_tokenizer(bos_token="(BOS)",eos_token="(EOS)"    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.get_image_processor(do_normalize=_A,padding_value=1.0    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    AlignProcessor.from_pretrained(
							    self.tmpdirname,bos_token="(BOS)",eos_token="(EOS)",do_normalize=_A,padding_value=1.0    )

							self.assertEqual(processor.tokenizer.get_vocab(),tokenizer_add_kwargs.get_vocab()    )
							self.assertIsInstance(processor.tokenizer,_A    )

							self.assertEqual(processor.image_processor.to_json_string(),image_processor_add_kwargs.to_json_string()    )
							self.assertIsInstance(processor.image_processor,_A    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		Dict				      =    AlignProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    image_processor(_A,return_tensors="np"    )
							SCREAMING_SNAKE_CASE_  :		str				      =    processor(images=_A,return_tensors="np"    )

							for key in input_image_proc.keys():
									self.assertAlmostEqual(input_image_proc[key].sum(),input_processor[key].sum(),delta=1E-2    )

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		int				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    AlignProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    "lower newer"

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    processor(text=_A    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    tokenizer(_A,padding="max_length",max_length=64    )

							for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key],encoded_processor[key]    )

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AlignProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    "lower newer"
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    processor(text=_A,images=_A    )

							self.assertListEqual(list(inputs.keys()    ),["input_ids", "token_type_ids", "attention_mask", "pixel_values"]    )

							# test if it raises when no input is passed
							with pytest.raises(_A    ):
									processor()

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		Any				      =    AlignProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							SCREAMING_SNAKE_CASE_  :		Any				      =    processor.batch_decode(_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    tokenizer.batch_decode(_A    )

							self.assertListEqual(_A,_A    )






					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		Any				      =    AlignProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		str				      =    "lower newer"
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    processor(text=_A,images=_A    )

							self.assertListEqual(list(inputs.keys()    ),processor.model_input_names    )

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)
