






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 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							def       __A (  self:   Optional[Any]   )    ->				Optional[int]:
												_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		,     __A   )
												with open(self.image_processor_file		,     '''w'''		,     encoding='''utf-8'''   ) as fp:
																	json.dump(__A		,     __A   )

							def       __A (  self:   List[Any]		,     **__A:   Any   )    ->				List[str]:
												return BertTokenizer.from_pretrained(self.tmpdirname		,     **__A   )

							def       __A (  self:   int		,     **__A:   List[str]   )    ->				Optional[Any]:
												return ViTImageProcessor.from_pretrained(self.tmpdirname		,     **__A   )

							def       __A (  self:   Union[str, Any]   )    ->				Tuple:
												shutil.rmtree(self.tmpdirname   )

							def       __A (  self:   Optional[int]   )    ->				Dict:
												_A								= [np.random.randint(2_55		,     size=(3, 30, 4_00)		,     dtype=np.uinta   )]

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

												return image_inputs

							def       __A (  self:   Any   )    ->				Dict:
												_A								= self.get_tokenizer()
												_A								= self.get_image_processor()

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

												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		,     __A   )

							def       __A (  self:   Union[str, Any]   )    ->				int:
												_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=__A		,     padding_value=1.0   )

												_A								= VisionTextDualEncoderProcessor.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		,     (BertTokenizer, BertTokenizerFast)   )

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

							def       __A (  self:   Optional[Any]   )    ->				Optional[int]:
												_A								= self.get_image_processor()
												_A								= self.get_tokenizer()

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

												_A								= self.prepare_image_inputs()

												_A								= image_processor(__A		,     return_tensors='''np'''   )
												_A								= processor(images=__A		,     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:   int   )    ->				List[str]:
												_A								= self.get_image_processor()
												_A								= self.get_tokenizer()

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

												_A								= '''lower newer'''

												_A								= processor(text=__A   )

												_A								= tokenizer(__A   )

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

							def       __A (  self:   Optional[int]   )    ->				Any:
												_A								= self.get_image_processor()
												_A								= self.get_tokenizer()

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

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

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

							def       __A (  self:   Union[str, Any]   )    ->				Union[str, Any]:
												_A								= self.get_image_processor()
												_A								= self.get_tokenizer()

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

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

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

												self.assertListEqual(__A		,     __A   )



							def       __A (  self:   Optional[int]   )    ->				Dict:
												_A								= self.get_image_processor()
												_A								= self.get_tokenizer()

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

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

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

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











__A      =				{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__A      =				{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= []

					for neighbour in graph[vert]:
										if not visited[neighbour]:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					order.append(_lowercase )

					return order




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= [vert]

					for neighbour in reversed_graph[vert]:
										if not visited[neighbour]:
															component += find_components(_lowercase					,			_lowercase					,			_lowercase )

					return component




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= len(_lowercase ) * [False]
					_A								= {vert: [] for vert in range(len(_lowercase ) )}

					for vert, neighbours in graph.items():
										for neighbour in neighbours:
															reversed_graph[neighbour].append(_lowercase )

					_A								= []
					for i, was_visited in enumerate(_lowercase ):
										if not was_visited:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					_A								= []
					_A								= len(_lowercase ) * [False]

					for i in range(len(_lowercase ) ):
										_A								= order[len(_lowercase ) - i - 1]
										if not visited[vert]:
															_A								= find_components(_lowercase					,			_lowercase					,			_lowercase )
															components_list.append(_lowercase )

					return components_list



