


"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
		from PIL import Image

		from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case      (  unittest.TestCase):







		def  a_ (					self      :       str		)       ->						str:





						'''simple docstring'''







						_A					  =			tempfile.mkdtemp()

						# fmt: off
						_A					  =			['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
						# fmt: on
						_A					  =			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]		)		)

						_A					  =			{
						    'do_resize': True,
						    'size': {'height': 18, 'width': 18},
						    'do_normalize': True,
						    'image_mean': [0.5, 0.5, 0.5],
						    'image_std': [0.5, 0.5, 0.5],
						}
						_A					  =			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      :       Any    ,   **a__      :       List[str]		)       ->						Optional[int]:





						'''simple docstring'''







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







		def  a_ (					self      :       Optional[Any]    ,   **a__      :       Dict		)       ->						Union[str, Any]:





						'''simple docstring'''







						return ViTImageProcessor.from_pretrained(self.tmpdirname    ,   **UpperCamelCase_		)







		def  a_ (					self      :       Union[str, Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						shutil.rmtree(self.tmpdirname		)







		def  a_ (					self      :       Tuple		)       ->						Any:





						'''simple docstring'''







						_A					  =			[np.random.randint(2_55    ,   size=(3, 30, 4_00)    ,   dtype=np.uinta		)]

						_A					  =			[Image.fromarray(np.moveaxis(UpperCamelCase_    ,   0    ,   -1		)		) for x in image_inputs]

						return image_inputs







		def  a_ (					self      :       List[Any]		)       ->						int:





						'''simple docstring'''







						_A					  =			self.get_tokenizer()
						_A					  =			self.get_image_processor()

						_A					  =			VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_    ,   image_processor=UpperCamelCase_		)

						processor.save_pretrained(self.tmpdirname		)
						_A					  =			VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname		)

						self.assertEqual(processor.tokenizer.get_vocab()    ,   tokenizer.get_vocab()		)
						self.assertIsInstance(processor.tokenizer    ,   (BertTokenizer, BertTokenizerFast)		)

						self.assertEqual(processor.image_processor.to_json_string()    ,   image_processor.to_json_string()		)
						self.assertIsInstance(processor.image_processor    ,   UpperCamelCase_		)







		def  a_ (					self      :       List[str]		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			VisionTextDualEncoderProcessor(
						    tokenizer=self.get_tokenizer()    ,   image_processor=self.get_image_processor()		)
						processor.save_pretrained(self.tmpdirname		)

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

						_A					  =			VisionTextDualEncoderProcessor.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    ,   (BertTokenizer, BertTokenizerFast)		)

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







		def  a_ (					self      :       Optional[int]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			self.get_image_processor()
						_A					  =			self.get_tokenizer()

						_A					  =			VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_    ,   image_processor=UpperCamelCase_		)

						_A					  =			self.prepare_image_inputs()

						_A					  =			image_processor(UpperCamelCase_    ,   return_tensors="np"		)
						_A					  =			processor(images=UpperCamelCase_    ,   return_tensors="np"		)

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







		def  a_ (					self      :       Dict		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			self.get_image_processor()
						_A					  =			self.get_tokenizer()

						_A					  =			VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_    ,   image_processor=UpperCamelCase_		)

						_A					  =			'lower newer'

						_A					  =			processor(text=UpperCamelCase_		)

						_A					  =			tokenizer(UpperCamelCase_		)

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







		def  a_ (					self      :       Tuple		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			self.get_image_processor()
						_A					  =			self.get_tokenizer()

						_A					  =			VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_    ,   image_processor=UpperCamelCase_		)

						_A					  =			'lower newer'
						_A					  =			self.prepare_image_inputs()

						_A					  =			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 self.assertRaises(UpperCamelCase_		):
										processor()







		def  a_ (					self      :       str		)       ->						Any:





						'''simple docstring'''







						_A					  =			self.get_image_processor()
						_A					  =			self.get_tokenizer()

						_A					  =			VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_    ,   image_processor=UpperCamelCase_		)

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

						_A					  =			processor.batch_decode(UpperCamelCase_		)
						_A					  =			tokenizer.batch_decode(UpperCamelCase_		)

						self.assertListEqual(UpperCamelCase_    ,   UpperCamelCase_		)







		def  a_ (					self      :       List[str]		)       ->						Dict:





						'''simple docstring'''







						_A					  =			self.get_image_processor()
						_A					  =			self.get_tokenizer()

						_A					  =			VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_    ,   image_processor=UpperCamelCase_		)

						_A					  =			'lower newer'
						_A					  =			self.prepare_image_inputs()

						_A					  =			processor(text=UpperCamelCase_    ,   images=UpperCamelCase_		)

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



"""simple docstring"""


def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )	->	str:
				# Return True if there is node that has not iterated.
				_A					  =			[False] * len(__lowercase   )
				_A					  =			[]
				queue.append(__lowercase   )
				_A					  =			True

				while queue:
								_A					  =			queue.pop(0   )
								for ind in range(len(graph[u]   )   ):
												if visited[ind] is False and graph[u][ind] > 0:
																queue.append(__lowercase   )
																_A					  =			True
																_A					  =			u

				return visited[t]






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	int:
				# This array is filled by BFS and to store path
				_A					  =			[-1] * (len(__lowercase   ))
				_A					  =			0
				while bfs(__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   ):
								_A					  =			float("Inf"   )
								_A					  =			sink

								while s != source:
												# Find the minimum value in select path
												_A					  =			min(__lowercase     ,			graph[parent[s]][s]   )
												_A					  =			parent[s]

								max_flow += path_flow
								_A					  =			sink

								while v != source:
												_A					  =			parent[v]
												graph[u][v] -= path_flow
												graph[v][u] += path_flow
												_A					  =			parent[v]
				return max_flow


a_       							=    [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

a_							,     a_       							=    0, 5




print(ford_fulkerson(graph, source, sink))