






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 							__UpperCamelCase     (  unittest.TestCase ):







	def 			_a (	self   :				str      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	tempfile.mkdtemp()

		# fmt: off
		__lowercase   							=	["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
		# fmt: on
		__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""": {"""height""": 18, """width""": 18},
		    """do_normalize""": True,
		    """image_mean""": [0.5, 0.5, 0.5],
		    """image_std""": [0.5, 0.5, 0.5],
		}
		__lowercase   							=	os.path.join(self.tmpdirname							,					__lowerCAmelCase      )
		with open(self.image_processor_file							,					"""w"""							,					encoding="""utf-8"""      ) as fp:
			json.dump(__lowerCAmelCase							,					__lowerCAmelCase      )







	def 			_a (	self   :				List[Any]							,					**_lowerCAmelCase   :				List[str]      )		->			List[str]:
		"""simple docstring"""







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







	def 			_a (	self   :				str							,					**_lowerCAmelCase   :				int      )		->			Union[str, Any]:
		"""simple docstring"""







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







	def 			_a (	self   :				Dict      )		->			List[Any]:
		"""simple docstring"""







		shutil.rmtree(self.tmpdirname      )







	def 			_a (	self   :				Any      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	[np.random.randint(255							,					size=(3, 30, 400)							,					dtype=np.uinta      )]

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

		return image_inputs







	def 			_a (	self   :				Tuple      )		->			Tuple:
		"""simple docstring"""







		__lowercase   							=	self.get_tokenizer()
		__lowercase   							=	self.get_image_processor()

		__lowercase   							=	VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase							,					image_processor=__lowerCAmelCase      )

		processor.save_pretrained(self.tmpdirname      )
		__lowercase   							=	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							,					__lowerCAmelCase      )







	def 			_a (	self   :				Optional[Any]      )		->			Dict:
		"""simple docstring"""







		__lowercase   							=	VisionTextDualEncoderProcessor(
		    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=__lowerCAmelCase							,					padding_value=1.0      )

		__lowercase   							=	VisionTextDualEncoderProcessor.from_pretrained(
		    self.tmpdirname							,					bos_token="""(BOS)"""							,					eos_token="""(EOS)"""							,					do_normalize=__lowerCAmelCase							,					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							,					__lowerCAmelCase      )







	def 			_a (	self   :				Any      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	self.get_image_processor()
		__lowercase   							=	self.get_tokenizer()

		__lowercase   							=	VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase							,					image_processor=__lowerCAmelCase      )

		__lowercase   							=	self.prepare_image_inputs()

		__lowercase   							=	image_processor(__lowerCAmelCase							,					return_tensors="""np"""      )
		__lowercase   							=	processor(images=__lowerCAmelCase							,					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   :				Optional[Any]      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	self.get_image_processor()
		__lowercase   							=	self.get_tokenizer()

		__lowercase   							=	VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase							,					image_processor=__lowerCAmelCase      )

		__lowercase   							=	"""lower newer"""

		__lowercase   							=	processor(text=__lowerCAmelCase      )

		__lowercase   							=	tokenizer(__lowerCAmelCase      )

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







	def 			_a (	self   :				Dict      )		->			str:
		"""simple docstring"""







		__lowercase   							=	self.get_image_processor()
		__lowercase   							=	self.get_tokenizer()

		__lowercase   							=	VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase							,					image_processor=__lowerCAmelCase      )

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

		__lowercase   							=	processor(text=__lowerCAmelCase							,					images=__lowerCAmelCase      )

		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(__lowerCAmelCase      ):
			processor()







	def 			_a (	self   :				str      )		->			int:
		"""simple docstring"""







		__lowercase   							=	self.get_image_processor()
		__lowercase   							=	self.get_tokenizer()

		__lowercase   							=	VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase							,					image_processor=__lowerCAmelCase      )

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

		__lowercase   							=	processor.batch_decode(__lowerCAmelCase      )
		__lowercase   							=	tokenizer.batch_decode(__lowerCAmelCase      )

		self.assertListEqual(__lowerCAmelCase							,					__lowerCAmelCase      )







	def 			_a (	self   :				int      )		->			int:
		"""simple docstring"""







		__lowercase   							=	self.get_image_processor()
		__lowercase   							=	self.get_tokenizer()

		__lowercase   							=	VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase							,					image_processor=__lowerCAmelCase      )

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

		__lowercase   							=	processor(text=__lowerCAmelCase							,					images=__lowerCAmelCase      )

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


'''simple docstring'''




# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def 							snake_case__				(     _A:   Dict					)       ->      str:

		'''simple docstring'''


		lowerCAmelCase     =    [False] * len(_A					)
		lowerCAmelCase     =    [-1] * len(_A					)

		def dfs(_A:   Dict ,  _A:   Tuple					):
				lowerCAmelCase     =    True
				lowerCAmelCase     =    c
				for u in graph[v]:
						if not visited[u]:
								dfs(_A ,  1 - c					)

		for i in range(len(_A					)					):
				if not visited[i]:
						dfs(_A ,  0					)

		for i in range(len(_A					)					):
				for j in graph[i]:
						if color[i] == color[j]:
								return False

		return True


# Adjacency list of graph
__lowercase        =    {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}




print(check_bipartite_dfs(graph))




