



'''simple docstring'''





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 				snake_case (unittest.TestCase   ):



       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						tempfile.mkdtemp()

           lowercase__		       =						[
               "[UNK]",
               "[CLS]",
               "[SEP]",
               "[PAD]",
               "[MASK]",
               "want",
               "##want",
               "##ed",
               "wa",
               "un",
               "runn",
               "##ing",
               ",",
               "low",
               "lowest",
           ]
           lowercase__		       =						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]      )      )

           lowercase__		       =						{
               "do_resize": True,
               "size": 20,
               "do_center_crop": True,
               "crop_size": 18,
               "do_normalize": True,
               "image_mean": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
               "image_std": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
           }
           lowercase__		       =						os.path.join(self.tmpdirname     ,UpperCAmelCase_      )
           with open(self.image_processor_file     ,"w"     ,encoding="utf-8"      ) as fp:
               json.dump(UpperCAmelCase_     ,UpperCAmelCase_      )



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							Any:
           return BertTokenizer.from_pretrained(self.tmpdirname     ,**UpperCAmelCase_      )



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							int:
           return BertTokenizerFast.from_pretrained(self.tmpdirname     ,**UpperCAmelCase_      )



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							int:
           return EfficientNetImageProcessor.from_pretrained(self.tmpdirname     ,**UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Dict:
           shutil.rmtree(self.tmpdirname      )



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						[np.random.randint(255     ,size=(3, 30, 400)     ,dtype=np.uinta      )]
           lowercase__		       =						[Image.fromarray(np.moveaxis(UpperCAmelCase_     ,0     ,-1      )      ) for x in image_inputs]
           return image_inputs



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						self.get_tokenizer()
           lowercase__		       =						self.get_rust_tokenizer()
           lowercase__		       =						self.get_image_processor()

           lowercase__		       =						AlignProcessor(tokenizer=UpperCAmelCase_     ,image_processor=UpperCAmelCase_      )
           processor_slow.save_pretrained(self.tmpdirname      )
           lowercase__		       =						AlignProcessor.from_pretrained(self.tmpdirname     ,use_fast=UpperCAmelCase_      )

           lowercase__		       =						AlignProcessor(tokenizer=UpperCAmelCase_     ,image_processor=UpperCAmelCase_      )
           processor_fast.save_pretrained(self.tmpdirname      )
           lowercase__		       =						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     ,UpperCAmelCase_      )
           self.assertIsInstance(processor_fast.tokenizer     ,UpperCAmelCase_      )

           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     ,UpperCAmelCase_      )
           self.assertIsInstance(processor_fast.image_processor     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						AlignProcessor(tokenizer=self.get_tokenizer()     ,image_processor=self.get_image_processor()      )
           processor.save_pretrained(self.tmpdirname      )

           lowercase__		       =						self.get_tokenizer(bos_token="(BOS)"     ,eos_token="(EOS)"      )
           lowercase__		       =						self.get_image_processor(do_normalize=UpperCAmelCase_     ,padding_value=1.0      )

           lowercase__		       =						AlignProcessor.from_pretrained(
               self.tmpdirname     ,bos_token="(BOS)"     ,eos_token="(EOS)"     ,do_normalize=UpperCAmelCase_     ,padding_value=1.0      )

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

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



       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						self.get_image_processor()
           lowercase__		       =						self.get_tokenizer()

           lowercase__		       =						AlignProcessor(tokenizer=UpperCAmelCase_     ,image_processor=UpperCAmelCase_      )

           lowercase__		       =						self.prepare_image_inputs()

           lowercase__		       =						image_processor(UpperCAmelCase_     ,return_tensors="np"      )
           lowercase__		       =						processor(images=UpperCAmelCase_     ,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 							_a      (				self      )  ->							int:
           lowercase__		       =						self.get_image_processor()
           lowercase__		       =						self.get_tokenizer()

           lowercase__		       =						AlignProcessor(tokenizer=UpperCAmelCase_     ,image_processor=UpperCAmelCase_      )

           lowercase__		       =						"lower newer"

           lowercase__		       =						processor(text=UpperCAmelCase_      )

           lowercase__		       =						tokenizer(UpperCAmelCase_     ,padding="max_length"     ,max_length=64      )

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



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						self.get_image_processor()
           lowercase__		       =						self.get_tokenizer()

           lowercase__		       =						AlignProcessor(tokenizer=UpperCAmelCase_     ,image_processor=UpperCAmelCase_      )

           lowercase__		       =						"lower newer"
           lowercase__		       =						self.prepare_image_inputs()

           lowercase__		       =						processor(text=UpperCAmelCase_     ,images=UpperCAmelCase_      )

           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(UpperCAmelCase_      ):
               processor()



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						self.get_image_processor()
           lowercase__		       =						self.get_tokenizer()

           lowercase__		       =						AlignProcessor(tokenizer=UpperCAmelCase_     ,image_processor=UpperCAmelCase_      )

           lowercase__		       =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowercase__		       =						processor.batch_decode(UpperCAmelCase_      )
           lowercase__		       =						tokenizer.batch_decode(UpperCAmelCase_      )

           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )







       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.get_image_processor()
           lowercase__		       =						self.get_tokenizer()

           lowercase__		       =						AlignProcessor(tokenizer=UpperCAmelCase_     ,image_processor=UpperCAmelCase_      )

           lowercase__		       =						"lower newer"
           lowercase__		       =						self.prepare_image_inputs()

           lowercase__		       =						processor(text=UpperCAmelCase_     ,images=UpperCAmelCase_      )

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









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						List[str]       ):  # noqa: E741





    '''simple docstring'''


    lowercase__		       =						len(_snake_case       )
    lowercase__		       =						0
    lowercase__		       =						[0] * n
    lowercase__		       =						[False] * n
    lowercase__		       =						[False] * n

    def dfs(_snake_case  :						Tuple     ,_snake_case  :						int     ,_snake_case  :						List[str]     ,_snake_case  :						str       ):
        if parent == root:
            out_edge_count += 1
        lowercase__		       =						True
        lowercase__		       =						at

        for to in l[at]:
            if to == parent:
                pass
            elif not visited[to]:
                lowercase__		       =						dfs(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       )
                lowercase__		       =						min(low[at]     ,low[to]       )

                # AP found via bridge
                if at < low[to]:
                    lowercase__		       =						True
                # AP found via cycle
                if at == low[to]:
                    lowercase__		       =						True
            else:
                lowercase__		       =						min(low[at]     ,_snake_case       )
        return out_edge_count

    for i in range(_snake_case       ):
        if not visited[i]:
            lowercase__		       =						0
            lowercase__		       =						dfs(_snake_case     ,_snake_case     ,-1     ,_snake_case       )
            lowercase__		       =						out_edge_count > 1

    for x in range(len(_snake_case       )       ):
        if is_art[x] is True:
            print(_snake_case       )


# Adjacency list of graph
SCREAMING_SNAKE_CASE__      					=     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}



compute_ap(data)




