




'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 OwlViTImageProcessor, OwlViTProcessor






@require_vision
class 		a							(  unittest.TestCase	):


				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							tempfile.mkdtemp()

									# fmt: off
									snake_case_		     =							['''''', '''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
									# fmt: on
									snake_case_		     =							dict(zip(lowercase_			,							range(len(lowercase_   )   )   )   )
									snake_case_		     =							['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
									snake_case_		     =							{'''unk_token''': '''<unk>'''}

									snake_case_		     =							os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES['''vocab_file''']   )
									snake_case_		     =							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(lowercase_   ) + '''\n'''   )
									with open(self.merges_file			,							'''w'''			,							encoding='''utf-8'''   ) as fp:
														fp.write('''\n'''.join(lowercase_   )   )

									snake_case_		     =							{
									    '''do_resize''': True,
									    '''size''': 20,
									    '''do_center_crop''': True,
									    '''crop_size''': 18,
									    '''do_normalize''': True,
									    '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
									    '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
									}
									snake_case_		     =							os.path.join(self.tmpdirname			,							lowercase_   )
									with open(self.image_processor_file			,							'''w'''			,							encoding='''utf-8'''   ) as fp:
														json.dump(lowercase_			,							lowercase_   )


				def     A_					(						self       :   Any			,							**lowercase_       :   Optional[int]   ):
									return CLIPTokenizer.from_pretrained(self.tmpdirname			,							pad_token='''!'''			,							**lowercase_   )


				def     A_					(						self       :   Dict			,							**lowercase_       :   Optional[Any]   ):
									return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,							pad_token='''!'''			,							**lowercase_   )


				def     A_					(						self       :   List[Any]			,							**lowercase_       :   Optional[Any]   ):
									return OwlViTImageProcessor.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   Any   ):
									shutil.rmtree(self.tmpdirname   )


				def     A_					(						self       :   str   ):
									snake_case_		     =							[np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta   )]

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

									return image_inputs


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_rust_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							OwlViTProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor_slow.save_pretrained(self.tmpdirname   )
									snake_case_		     =							OwlViTProcessor.from_pretrained(self.tmpdirname			,							use_fast=lowercase_   )

									snake_case_		     =							OwlViTProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor_fast.save_pretrained(self.tmpdirname   )
									snake_case_		     =							OwlViTProcessor.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			,							lowercase_   )
									self.assertIsInstance(processor_fast.tokenizer			,							lowercase_   )

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


				def     A_					(						self       :   Any   ):
									snake_case_		     =							OwlViTProcessor(tokenizer=self.get_tokenizer()			,							image_processor=self.get_image_processor()   )
									processor.save_pretrained(self.tmpdirname   )

									snake_case_		     =							self.get_tokenizer(bos_token='''(BOS)'''			,							eos_token='''(EOS)'''   )
									snake_case_		     =							self.get_image_processor(do_normalize=lowercase_   )

									snake_case_		     =							OwlViTProcessor.from_pretrained(
									    self.tmpdirname			,							bos_token='''(BOS)'''			,							eos_token='''(EOS)'''			,							do_normalize=lowercase_   )

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

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


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							OwlViTProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							image_processor(lowercase_			,							return_tensors='''np'''   )
									snake_case_		     =							processor(images=lowercase_			,							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       :   Union[str, Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							OwlViTProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'''lower newer'''

									snake_case_		     =							processor(text=lowercase_			,							return_tensors='''np'''   )

									snake_case_		     =							tokenizer(lowercase_			,							return_tensors='''np'''   )

									for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key][0].tolist()			,							encoded_processor[key][0].tolist()   )


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							OwlViTProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

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

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

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

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


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							'''google/owlvit-base-patch32'''
									snake_case_		     =							OwlViTProcessor.from_pretrained(lowercase_   )

									snake_case_		     =							['''cat''', '''nasa badge''']
									snake_case_		     =							processor(text=lowercase_   )

									snake_case_		     =							16
									self.assertListEqual(list(inputs.keys()   )			,							['''input_ids''', '''attention_mask''']   )
									self.assertEqual(inputs['''input_ids'''].shape			,							(2, seq_length)   )

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


				def     A_					(						self       :   Any   ):
									snake_case_		     =							'''google/owlvit-base-patch32'''
									snake_case_		     =							OwlViTProcessor.from_pretrained(lowercase_   )

									snake_case_		     =							[['''cat''', '''nasa badge'''], ['''person''']]
									snake_case_		     =							processor(text=lowercase_   )

									snake_case_		     =							16
									snake_case_		     =							len(lowercase_   )
									snake_case_		     =							max([len(lowercase_   ) for texts in input_texts]   )

									self.assertListEqual(list(inputs.keys()   )			,							['''input_ids''', '''attention_mask''']   )
									self.assertEqual(inputs['''input_ids'''].shape			,							(batch_size * num_max_text_queries, seq_length)   )

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


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							'''google/owlvit-base-patch32'''
									snake_case_		     =							OwlViTProcessor.from_pretrained(lowercase_   )

									snake_case_		     =							['''cat''', '''nasa badge''']
									snake_case_		     =							processor(text=lowercase_   )

									snake_case_		     =							16
									snake_case_		     =							inputs['''input_ids''']
									snake_case_		     =							[
									    [4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
									    [4_9406, 6841, 1_1301, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
									]

									self.assertListEqual(list(inputs.keys()   )			,							['''input_ids''', '''attention_mask''']   )
									self.assertEqual(inputs['''input_ids'''].shape			,							(2, seq_length)   )
									self.assertListEqual(list(input_ids[0]   )			,							predicted_ids[0]   )
									self.assertListEqual(list(input_ids[1]   )			,							predicted_ids[1]   )


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							OwlViTProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							self.prepare_image_inputs()
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(images=lowercase_			,							query_images=lowercase_   )

									self.assertListEqual(list(inputs.keys()   )			,							['''query_pixel_values''', '''pixel_values''']   )

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


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							OwlViTProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

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

									snake_case_		     =							processor.batch_decode(lowercase_   )
									snake_case_		     =							tokenizer.batch_decode(lowercase_   )

									self.assertListEqual(lowercase_			,							lowercase_   )







'''simple docstring'''

def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					snake_case_		     =							[False] * len(__UpperCAmelCase      )
					snake_case_		     =							[]
					queue.append(__UpperCAmelCase      )
					snake_case_		     =							True

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

					return visited[t]


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Tuple:

					'''simple docstring'''

					snake_case_		     =							[-1] * (len(__UpperCAmelCase      ))
					snake_case_		     =							0
					while bfs(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      ):
										snake_case_		     =							float('''Inf'''      )
										snake_case_		     =							sink

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

										max_flow += path_flow
										snake_case_		     =							sink

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


a    :							List[Any]										=    [
    [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    :							int										=    0, 5

print(ford_fulkerson(graph, source, sink))

